Merge pull request #1074 from myhloli/dev

feat(demo): add visualization bbox parameter and refactor parsing process

demo/demo2.json

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Note that individual disparities can be converted"}, {"category_id": 15, "poly": [864.0, 561.0, 1568.0, 561.0, 1568.0, 594.0, 864.0, 594.0], "score": 0.98, "text": "to actual depths if the geometry of the camera setup is"}, {"category_id": 15, "poly": [859.0, 587.0, 1568.0, 591.0, 1568.0, 630.0, 859.0, 626.0], "score": 0.98, "text": " known, i.e., the stereo configuration of cameras has been pre-"}, {"category_id": 15, "poly": [862.0, 626.0, 984.0, 626.0, 984.0, 658.0, 862.0, 658.0], "score": 1.0, "text": "calibrated."}, {"category_id": 15, "poly": [155.0, 921.0, 839.0, 924.0, 838.0, 963.0, 155.0, 960.0], "score": 0.98, "text": " Modern stereo matching algorithms achieve excellent results"}, {"category_id": 15, "poly": [127.0, 956.0, 838.0, 958.0, 838.0, 997.0, 127.0, 995.0], "score": 0.98, "text": " on static stereo images, as demonstrated by the Middlebury"}, {"category_id": 15, "poly": [132.0, 995.0, 836.0, 995.0, 836.0, 1027.0, 132.0, 1027.0], "score": 0.98, "text": "stereo performance benchmark [1], [2]. However, their ap-"}, {"category_id": 15, "poly": [134.0, 1027.0, 834.0, 1027.0, 834.0, 1059.0, 134.0, 1059.0], "score": 1.0, "text": "plication to stereo video sequences does not guarantee inter-"}, {"category_id": 15, "poly": [134.0, 1061.0, 836.0, 1061.0, 836.0, 1093.0, 134.0, 1093.0], "score": 0.99, "text": "frame consistency of matches extracted from subsequent stereo"}, {"category_id": 15, "poly": [132.0, 1095.0, 838.0, 1095.0, 838.0, 1125.0, 132.0, 1125.0], "score": 0.99, "text": "frame pairs. The lack of temporal consistency of matches"}, {"category_id": 15, "poly": [134.0, 1128.0, 836.0, 1128.0, 836.0, 1157.0, 134.0, 1157.0], "score": 1.0, "text": "between successive frames introduces spurious artifacts in the"}, {"category_id": 15, "poly": [132.0, 1160.0, 836.0, 1160.0, 836.0, 1192.0, 132.0, 1192.0], "score": 0.99, "text": "resulting disparity maps. The problem of obtaining temporally"}, {"category_id": 15, "poly": [132.0, 1194.0, 838.0, 1194.0, 838.0, 1226.0, 132.0, 1226.0], "score": 0.98, "text": "consistent sequences of disparity maps from video streams is"}, {"category_id": 15, "poly": [134.0, 1228.0, 838.0, 1228.0, 838.0, 1260.0, 134.0, 1260.0], "score": 0.98, "text": "known as the temporal stereo correspondence problem, yet"}, {"category_id": 15, "poly": [129.0, 1258.0, 841.0, 1260.0, 841.0, 1293.0, 129.0, 1290.0], "score": 0.98, "text": "the amount of research efforts oriented towards finding an"}, {"category_id": 15, "poly": [134.0, 1292.0, 760.0, 1292.0, 760.0, 1325.0, 134.0, 1325.0], "score": 0.99, "text": "effective solution to this problem is surprisingly small."}, {"category_id": 15, "poly": [157.0, 1320.0, 836.0, 1322.0, 836.0, 1361.0, 157.0, 1359.0], "score": 0.98, "text": " A method is proposed for real-time temporal stereo match-"}, {"category_id": 15, "poly": [134.0, 1361.0, 836.0, 1361.0, 836.0, 1393.0, 134.0, 1393.0], "score": 1.0, "text": "ing that efficiently propagates matching cost information be-"}, {"category_id": 15, "poly": [134.0, 1393.0, 836.0, 1393.0, 836.0, 1425.0, 134.0, 1425.0], "score": 0.99, "text": "tween consecutive frames of a stereo video sequence. This"}, {"category_id": 15, "poly": [132.0, 1423.0, 834.0, 1425.0, 834.0, 1458.0, 132.0, 1455.0], "score": 0.98, "text": "method is invariant to the number of prior frames being"}, {"category_id": 15, "poly": [134.0, 1458.0, 836.0, 1458.0, 836.0, 1490.0, 134.0, 1490.0], "score": 0.99, "text": "considered, and can be easily incorporated into any local stereo"}, {"category_id": 15, "poly": [132.0, 1492.0, 836.0, 1492.0, 836.0, 1524.0, 132.0, 1524.0], "score": 0.98, "text": "method based on edge-aware filters. The iterative adaptive"}, {"category_id": 15, "poly": [132.0, 1526.0, 838.0, 1526.0, 838.0, 1558.0, 132.0, 1558.0], "score": 0.99, "text": "support matching algorithm presented in [3] serves as a"}, {"category_id": 15, "poly": [132.0, 1558.0, 557.0, 1558.0, 557.0, 1590.0, 132.0, 1590.0], "score": 0.99, "text": "foundation for the proposed method."}, {"category_id": 15, "poly": [887.0, 1483.0, 1571.0, 1485.0, 1571.0, 1524.0, 887.0, 1522.0], "score": 0.98, "text": " In contrast, local methods, which are typically built upon"}, {"category_id": 15, "poly": [859.0, 1517.0, 1573.0, 1519.0, 1573.0, 1558.0, 859.0, 1556.0], "score": 0.97, "text": " the Winner-Takes-All (WTA) framework, have the property of "}, {"category_id": 15, "poly": [864.0, 1556.0, 1566.0, 1556.0, 1566.0, 1588.0, 864.0, 1588.0], "score": 0.99, "text": "computational regularity and are thus suitable for implemen-"}, {"category_id": 15, "poly": [862.0, 1588.0, 1566.0, 1588.0, 1566.0, 1620.0, 862.0, 1620.0], "score": 1.0, "text": "tation on parallel graphics hardware. Within the WTA frame-"}, {"category_id": 15, "poly": [862.0, 1616.0, 1568.0, 1618.0, 1568.0, 1657.0, 862.0, 1655.0], "score": 0.98, "text": "work, local stereo algorithms consider a range of disparity"}, {"category_id": 15, "poly": [864.0, 1655.0, 1566.0, 1655.0, 1566.0, 1687.0, 864.0, 1687.0], "score": 0.98, "text": "hypotheses and compute a volume of pixel-wise dissimilarity"}, {"category_id": 15, "poly": [862.0, 1689.0, 1571.0, 1689.0, 1571.0, 1721.0, 862.0, 1721.0], "score": 0.99, "text": "metrics between the reference image and the matched image at"}, {"category_id": 15, "poly": [862.0, 1723.0, 1568.0, 1721.0, 1568.0, 1753.0, 862.0, 1755.0], "score": 0.99, "text": "every considered disparity value. Final disparities are chosen"}, {"category_id": 15, "poly": [864.0, 1755.0, 1568.0, 1755.0, 1568.0, 1785.0, 864.0, 1785.0], "score": 1.0, "text": "from the cost volume by traversing through its values and"}, {"category_id": 15, "poly": [866.0, 1788.0, 1568.0, 1788.0, 1568.0, 1820.0, 866.0, 1820.0], "score": 0.99, "text": "selecting the disparities associated with minimum matching"}, {"category_id": 15, "poly": [859.0, 1817.0, 1377.0, 1820.0, 1377.0, 1859.0, 859.0, 1856.0], "score": 0.98, "text": " costs for every pixel of the reference image."}, {"category_id": 15, "poly": [885.0, 1187.0, 1571.0, 1187.0, 1571.0, 1226.0, 885.0, 1226.0], "score": 0.97, "text": " In their excellent taxonomy paper [1], Scharstein and"}, {"category_id": 15, "poly": [864.0, 1224.0, 1566.0, 1224.0, 1566.0, 1254.0, 864.0, 1254.0], "score": 0.99, "text": "Szeliski classify stereo algorithms as local or global meth-"}, {"category_id": 15, "poly": [859.0, 1249.0, 1571.0, 1254.0, 1570.0, 1293.0, 859.0, 1288.0], "score": 0.99, "text": " ods. Global methods, which offer outstanding accuracy, are"}, {"category_id": 15, "poly": [862.0, 1288.0, 1571.0, 1288.0, 1571.0, 1327.0, 862.0, 1327.0], "score": 0.98, "text": "typically derived from an energy minimization framework"}, {"category_id": 15, "poly": [859.0, 1322.0, 1566.0, 1322.0, 1566.0, 1352.0, 859.0, 1352.0], "score": 0.99, "text": "that allows for explicit integration of disparity smoothness"}, {"category_id": 15, "poly": [864.0, 1357.0, 1568.0, 1357.0, 1568.0, 1389.0, 864.0, 1389.0], "score": 0.99, "text": "constraints and thus is capable of regularizing the solution"}, {"category_id": 15, "poly": [864.0, 1391.0, 1568.0, 1391.0, 1568.0, 1421.0, 864.0, 1421.0], "score": 1.0, "text": "in weakly textured areas. The minimization, however, is often"}, {"category_id": 15, "poly": [864.0, 1423.0, 1568.0, 1423.0, 1568.0, 1455.0, 864.0, 1455.0], "score": 0.99, "text": "achieved using iterative methods or graph cuts, which do not"}, {"category_id": 15, "poly": [864.0, 1458.0, 1418.0, 1458.0, 1418.0, 1487.0, 864.0, 1487.0], "score": 0.99, "text": "lend themselves well to parallel implementation."}, {"category_id": 15, "poly": [155.0, 1650.0, 839.0, 1652.0, 838.0, 1691.0, 155.0, 1689.0], "score": 0.97, "text": " Stereo matching is the process of identifying correspon-"}, {"category_id": 15, "poly": [134.0, 1687.0, 838.0, 1687.0, 838.0, 1719.0, 134.0, 1719.0], "score": 0.99, "text": "dences between pixels in stereo images obtained using a"}, {"category_id": 15, "poly": [132.0, 1723.0, 838.0, 1721.0, 838.0, 1753.0, 132.0, 1755.0], "score": 0.98, "text": "pair of synchronized cameras. These correspondences are"}, {"category_id": 15, "poly": [134.0, 1755.0, 836.0, 1755.0, 836.0, 1788.0, 134.0, 1788.0], "score": 0.99, "text": "conveniently represented using the notion of disparity, i.e. the"}, {"category_id": 15, "poly": [134.0, 1788.0, 836.0, 1788.0, 836.0, 1820.0, 134.0, 1820.0], "score": 1.0, "text": "positional offset between two matching pixels. It is assumed"}, {"category_id": 15, "poly": [134.0, 1822.0, 836.0, 1822.0, 836.0, 1854.0, 134.0, 1854.0], "score": 0.99, "text": "that the stereo images are rectified, such that matching pixels"}, {"category_id": 15, "poly": [132.0, 1854.0, 836.0, 1854.0, 836.0, 1886.0, 132.0, 1886.0], "score": 0.99, "text": "are confined within corresponding rows of the images and"}, {"category_id": 15, "poly": [134.0, 1888.0, 838.0, 1888.0, 838.0, 1918.0, 134.0, 1918.0], "score": 1.0, "text": "thus disparities are restricted to the horizontal dimension, as"}, {"category_id": 15, "poly": [134.0, 1920.0, 838.0, 1920.0, 838.0, 1952.0, 134.0, 1952.0], "score": 1.0, "text": "illustrated in Figure 1. For visualization purposes, disparities"}, {"category_id": 15, "poly": [134.0, 1955.0, 838.0, 1955.0, 838.0, 1987.0, 134.0, 1987.0], "score": 0.99, "text": "recovered for every pixel of a reference image are stored"}, {"category_id": 15, "poly": [129.0, 1985.0, 841.0, 1982.0, 841.0, 2021.0, 129.0, 2024.0], "score": 0.98, "text": "together in the form of an image, which is known as the"}, {"category_id": 15, "poly": [370.0, 885.0, 594.0, 885.0, 594.0, 917.0, 370.0, 917.0], "score": 1.0, "text": "1. INTRODUCTION"}, {"category_id": 15, "poly": [638.0, 2099.0, 1062.0, 2099.0, 1062.0, 2131.0, 638.0, 2131.0], "score": 0.98, "text": "978-1-4673-5208-6/13/$31.00 @2013 IEEE"}, {"category_id": 15, "poly": [374.0, 1613.0, 591.0, 1613.0, 591.0, 1645.0, 374.0, 1645.0], "score": 0.95, "text": "II. BACKGROUND"}, {"category_id": 15, "poly": [859.0, 992.0, 1571.0, 995.0, 1571.0, 1034.0, 859.0, 1031.0], "score": 0.99, "text": " Figure 1: Geometry of two horizontally aligned views where p"}, {"category_id": 15, "poly": [864.0, 1098.0, 1291.0, 1098.0, 1291.0, 1130.0, 864.0, 1130.0], "score": 0.99, "text": "them along the horizontal dimension."}, {"category_id": 15, "poly": [859.0, 1061.0, 1194.0, 1059.0, 1194.0, 1098.0, 859.0, 1100.0], "score": 0.98, "text": " pixel in the target frame, and"}, {"category_id": 15, "poly": [1227.0, 1061.0, 1571.0, 1059.0, 1571.0, 1098.0, 1227.0, 1100.0], "score": 0.97, "text": " denotes the disparity between"}, {"category_id": 15, "poly": [864.0, 1034.0, 1303.0, 1034.0, 1303.0, 1063.0, 864.0, 1063.0], "score": 0.99, "text": "denotes a pixel in the reference frame,"}, {"category_id": 15, "poly": [1328.0, 1034.0, 1566.0, 1034.0, 1566.0, 1063.0, 1328.0, 1063.0], "score": 0.96, "text": " denotes its matching"}, {"category_id": 15, "poly": [508.0, 357.0, 1194.0, 360.0, 1194.0, 392.0, 508.0, 390.0], "score": 0.98, "text": "Jedrzej Kowalczuk, Eric T. Psota, and Lance C. P\u00e9rez"}, {"category_id": 15, "poly": [443.0, 392.0, 1245.0, 392.0, 1245.0, 424.0, 443.0, 424.0], "score": 0.99, "text": "Department of Electrical Engineering, University of Nebraska-Lincoln"}, {"category_id": 15, "poly": [614.0, 435.0, 1081.0, 435.0, 1081.0, 465.0, 614.0, 465.0], "score": 0.99, "text": "[jkowalczuk2,epsota,lperez] @unl.edu"}, {"category_id": 15, "poly": [159.0, 527.0, 836.0, 527.0, 836.0, 559.0, 159.0, 559.0], "score": 0.98, "text": "Abstract-Stereo matching algorithms are nearly always de-"}, {"category_id": 15, "poly": [132.0, 555.0, 838.0, 555.0, 838.0, 587.0, 132.0, 587.0], "score": 0.98, "text": "signed to find matches between a single pair of images. A method"}, {"category_id": 15, "poly": [134.0, 580.0, 836.0, 580.0, 836.0, 612.0, 134.0, 612.0], "score": 1.0, "text": "is presented that was specifically designed to operate on sequences"}, {"category_id": 15, "poly": [132.0, 605.0, 838.0, 607.0, 838.0, 646.0, 132.0, 644.0], "score": 0.99, "text": "of images. This method considers the cost of matching image"}, {"category_id": 15, "poly": [132.0, 637.0, 838.0, 637.0, 838.0, 669.0, 132.0, 669.0], "score": 0.98, "text": "points in both the spatial and temporal domain. To maintain"}, {"category_id": 15, "poly": [134.0, 667.0, 838.0, 667.0, 838.0, 699.0, 134.0, 699.0], "score": 0.97, "text": "real-time operation, a temporal cost aggregation method is used"}, {"category_id": 15, "poly": [132.0, 692.0, 836.0, 692.0, 836.0, 722.0, 132.0, 722.0], "score": 0.98, "text": "to evaluate the likelihood of matches that is invariant with respect"}, {"category_id": 15, "poly": [127.0, 717.0, 841.0, 715.0, 841.0, 754.0, 127.0, 756.0], "score": 0.97, "text": "to the number of prior images being considered. This method"}, {"category_id": 15, "poly": [127.0, 742.0, 841.0, 745.0, 841.0, 784.0, 127.0, 781.0], "score": 0.98, "text": "has been implemented on massively parallel GPU hardware,"}, {"category_id": 15, "poly": [132.0, 777.0, 838.0, 777.0, 838.0, 809.0, 132.0, 809.0], "score": 0.99, "text": "and the implementation ranks as one of the fastest and most"}, {"category_id": 15, "poly": [132.0, 802.0, 838.0, 804.0, 838.0, 836.0, 132.0, 834.0], "score": 0.99, "text": "accurate real-time stereo matching methods as measured by the"}, {"category_id": 15, "poly": [134.0, 830.0, 619.0, 830.0, 619.0, 862.0, 134.0, 862.0], "score": 0.99, "text": "Middlebury stereo performance benchmark."}, {"category_id": 15, "poly": [887.0, 1849.0, 1568.0, 1852.0, 1568.0, 1891.0, 887.0, 1888.0], "score": 0.99, "text": " Disparity maps obtained using this simple strategy are often"}, {"category_id": 15, "poly": [862.0, 1888.0, 1568.0, 1888.0, 1568.0, 1920.0, 862.0, 1920.0], "score": 0.98, "text": "too noisy to be considered useable. To reduce the effects"}, {"category_id": 15, "poly": [864.0, 1923.0, 1568.0, 1923.0, 1568.0, 1952.0, 864.0, 1952.0], "score": 0.99, "text": "of noise and enforce spatial consistency of matches, local"}, {"category_id": 15, "poly": [862.0, 1948.0, 1568.0, 1950.0, 1568.0, 1989.0, 861.0, 1987.0], "score": 0.99, "text": "stereo algorithms consider arbitrarily shaped and sized support"}, {"category_id": 15, "poly": [864.0, 1989.0, 1568.0, 1989.0, 1568.0, 2021.0, 864.0, 2021.0], "score": 0.99, "text": "windows centered at each pixel of the reference image, and"}], "page_info": {"page_no": 0, "height": 2200, "width": 1700}}, {"layout_dets": [{"category_id": 8, "poly": [962.3624267578125, 1513.2073974609375, 1465.4017333984375, 1513.2073974609375, 1465.4017333984375, 1669.1397705078125, 962.3624267578125, 1669.1397705078125], "score": 0.9999995231628418}, {"category_id": 9, "poly": [1530.72998046875, 1101.879638671875, 1565.2568359375, 1101.879638671875, 1565.2568359375, 1130.8609619140625, 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Here, the truncation of color difference for the red,"}, {"category_id": 15, "poly": [866.0, 1719.0, 1547.0, 1719.0, 1547.0, 1749.0, 866.0, 1749.0], "score": 0.99, "text": "sum of truncated absolute color differences between pixels"}, {"category_id": 15, "poly": [864.0, 163.0, 1568.0, 163.0, 1568.0, 192.0, 864.0, 192.0], "score": 1.0, "text": "temporal information, making it possible to process a temporal"}, {"category_id": 15, "poly": [859.0, 188.0, 1571.0, 193.0, 1570.0, 229.0, 859.0, 225.0], "score": 0.99, "text": " collection of cost volumes. The filtering operation was shown"}, {"category_id": 15, "poly": [864.0, 229.0, 1566.0, 229.0, 1566.0, 261.0, 864.0, 261.0], "score": 0.99, "text": "to preserve spatio-temporal edges present in the cost volumes,"}, {"category_id": 15, "poly": [859.0, 261.0, 1564.0, 264.0, 1564.0, 296.0, 859.0, 293.0], "score": 0.98, "text": " resulting in increased temporal consistency of disparity maps,"}, {"category_id": 15, "poly": [864.0, 296.0, 1566.0, 296.0, 1566.0, 328.0, 864.0, 328.0], "score": 0.99, "text": "greater robustness to image noise, and more accurate behavior"}, {"category_id": 15, "poly": [866.0, 328.0, 1160.0, 328.0, 1160.0, 360.0, 866.0, 360.0], "score": 1.0, "text": "around object boundaries."}, {"category_id": 15, "poly": [129.0, 158.0, 841.0, 153.0, 841.0, 192.0, 130.0, 197.0], "score": 0.99, "text": "aggregate cost values within the pixel neighborhoods defined"}, {"category_id": 15, "poly": [129.0, 188.0, 841.0, 190.0, 841.0, 229.0, 129.0, 227.0], "score": 0.99, "text": "by these windows. In 2005, Yoon and Kweon [4] proposed"}, {"category_id": 15, "poly": [132.0, 229.0, 838.0, 229.0, 838.0, 261.0, 132.0, 261.0], "score": 1.0, "text": "an adaptive matching cost aggregation scheme, which assigns"}, {"category_id": 15, "poly": [132.0, 261.0, 838.0, 261.0, 838.0, 293.0, 132.0, 293.0], "score": 0.98, "text": "a weight value to every pixel located in the support window"}, {"category_id": 15, "poly": [132.0, 293.0, 838.0, 293.0, 838.0, 325.0, 132.0, 325.0], "score": 0.98, "text": "of a given pixel of interest. The weight value is based on"}, {"category_id": 15, "poly": [132.0, 328.0, 836.0, 328.0, 836.0, 360.0, 132.0, 360.0], "score": 0.99, "text": "the spatial and color similarity between the pixel of interest"}, {"category_id": 15, "poly": [134.0, 360.0, 836.0, 360.0, 836.0, 392.0, 134.0, 392.0], "score": 1.0, "text": "and a pixel in its support window, and the aggregated cost is"}, {"category_id": 15, "poly": [134.0, 394.0, 836.0, 394.0, 836.0, 426.0, 134.0, 426.0], "score": 0.99, "text": "computed as a weighted average of the pixel-wise costs within"}, {"category_id": 15, "poly": [127.0, 422.0, 839.0, 424.0, 838.0, 463.0, 127.0, 461.0], "score": 0.98, "text": " the considered support window. The edge-preserving nature"}, {"category_id": 15, "poly": [129.0, 456.0, 838.0, 454.0, 838.0, 493.0, 129.0, 495.0], "score": 0.99, "text": " and matching accuracy of adaptive support weights have made"}, {"category_id": 15, "poly": [132.0, 490.0, 841.0, 490.0, 841.0, 529.0, 132.0, 529.0], "score": 0.99, "text": "them one of the most popular choices for cost aggregation in"}, {"category_id": 15, "poly": [132.0, 527.0, 797.0, 527.0, 797.0, 559.0, 132.0, 559.0], "score": 0.97, "text": "recently proposed stereo matching algorithms [3], [5]-[8]."}, {"category_id": 15, "poly": [157.0, 958.0, 836.0, 958.0, 836.0, 988.0, 157.0, 988.0], "score": 0.99, "text": "It has been demonstrated that the performance of stereo"}, {"category_id": 15, "poly": [132.0, 990.0, 838.0, 990.0, 838.0, 1022.0, 132.0, 1022.0], "score": 0.99, "text": "algorithms designed to match a single pair of images can"}, {"category_id": 15, "poly": [132.0, 1024.0, 836.0, 1024.0, 836.0, 1056.0, 132.0, 1056.0], "score": 0.99, "text": "be adapted to take advantage of the temporal dependencies"}, {"category_id": 15, "poly": [129.0, 1054.0, 838.0, 1054.0, 838.0, 1093.0, 129.0, 1093.0], "score": 0.97, "text": "available in stereo video sequences. Early proposed solutions"}, {"category_id": 15, "poly": [132.0, 1091.0, 836.0, 1091.0, 836.0, 1123.0, 132.0, 1123.0], "score": 0.99, "text": "to temporal stereo matching attempted to average matching"}, {"category_id": 15, "poly": [134.0, 1123.0, 836.0, 1123.0, 836.0, 1155.0, 134.0, 1155.0], "score": 0.99, "text": "costs across subsequent frames of a video sequence [13],"}, {"category_id": 15, "poly": [129.0, 1153.0, 841.0, 1150.0, 841.0, 1189.0, 129.0, 1192.0], "score": 0.98, "text": "[14]. Attempts have been made to integrate estimation of"}, {"category_id": 15, "poly": [134.0, 1192.0, 838.0, 1192.0, 838.0, 1224.0, 134.0, 1224.0], "score": 0.99, "text": "motion fields (optical flow) into temporal stereo matching. The"}, {"category_id": 15, "poly": [132.0, 1224.0, 838.0, 1224.0, 838.0, 1256.0, 132.0, 1256.0], "score": 0.99, "text": "methods of [15] and [16] perform smoothing of disparities"}, {"category_id": 15, "poly": [129.0, 1254.0, 841.0, 1254.0, 841.0, 1292.0, 129.0, 1292.0], "score": 0.99, "text": " along motion vectors recovered from the video sequence. The"}, {"category_id": 15, "poly": [132.0, 1290.0, 838.0, 1290.0, 838.0, 1322.0, 132.0, 1322.0], "score": 0.99, "text": "estimation of the motion field, however, prevents real-time"}, {"category_id": 15, "poly": [132.0, 1325.0, 838.0, 1325.0, 838.0, 1354.0, 132.0, 1354.0], "score": 0.99, "text": "implementation, since state-of-the-art optical flow algorithms"}, {"category_id": 15, "poly": [129.0, 1354.0, 841.0, 1354.0, 841.0, 1393.0, 129.0, 1393.0], "score": 0.99, "text": " do not, in general, approach real-time frame rates. In a related"}, {"category_id": 15, "poly": [129.0, 1386.0, 841.0, 1384.0, 841.0, 1423.0, 129.0, 1425.0], "score": 0.99, "text": "approach, Sizintsev and Wildes [17], [18] used steerable"}, {"category_id": 15, "poly": [134.0, 1423.0, 836.0, 1423.0, 836.0, 1455.0, 134.0, 1455.0], "score": 0.99, "text": "filters to obtain descriptors characterizing motion of image"}, {"category_id": 15, "poly": [134.0, 1455.0, 836.0, 1455.0, 836.0, 1487.0, 134.0, 1487.0], "score": 0.99, "text": "features in both space and time. Unlike traditional algorithms,"}, {"category_id": 15, "poly": [132.0, 1490.0, 838.0, 1490.0, 838.0, 1522.0, 132.0, 1522.0], "score": 0.98, "text": "their method performs matching on spatio-temporal motion"}, {"category_id": 15, "poly": [129.0, 1519.0, 841.0, 1517.0, 841.0, 1556.0, 129.0, 1558.0], "score": 0.99, "text": " descriptors, rather than on pure pixel intensity values, which"}, {"category_id": 15, "poly": [132.0, 1554.0, 841.0, 1554.0, 841.0, 1593.0, 132.0, 1593.0], "score": 0.99, "text": "leads to improved temporal coherence of disparity maps at the"}, {"category_id": 15, "poly": [132.0, 1586.0, 698.0, 1586.0, 698.0, 1618.0, 132.0, 1618.0], "score": 0.99, "text": "cost of reduced accuracy at depth discontinuities."}, {"category_id": 15, "poly": [159.0, 559.0, 838.0, 559.0, 838.0, 591.0, 159.0, 591.0], "score": 0.99, "text": "Recently, Rheman et al. [9], [10] have revisited the cost"}, {"category_id": 15, "poly": [132.0, 594.0, 838.0, 589.0, 839.0, 621.0, 132.0, 626.0], "score": 1.0, "text": "aggregation step of stereo algorithms, and demonstrated that"}, {"category_id": 15, "poly": [132.0, 626.0, 838.0, 626.0, 838.0, 658.0, 132.0, 658.0], "score": 0.99, "text": "cost aggregation can be performed by filtering of subsequent"}, {"category_id": 15, "poly": [134.0, 660.0, 834.0, 660.0, 834.0, 692.0, 134.0, 692.0], "score": 1.0, "text": "layers of the initially computed matching cost volume. In par-"}, {"category_id": 15, "poly": [132.0, 692.0, 836.0, 692.0, 836.0, 724.0, 132.0, 724.0], "score": 0.99, "text": "ticular, the edge-aware image filters, such as the bilateral filter"}, {"category_id": 15, "poly": [127.0, 719.0, 839.0, 724.0, 838.0, 761.0, 127.0, 756.0], "score": 0.99, "text": " of Tomasi and Manducci [11] or the guided filter of He [12],"}, {"category_id": 15, "poly": [132.0, 759.0, 838.0, 759.0, 838.0, 791.0, 132.0, 791.0], "score": 0.98, "text": "have been rendered useful for the problem of matching cost"}, {"category_id": 15, "poly": [132.0, 793.0, 838.0, 791.0, 838.0, 823.0, 132.0, 825.0], "score": 0.99, "text": "aggregation, enabling stereo algorithms to correctly recover"}, {"category_id": 15, "poly": [134.0, 825.0, 838.0, 825.0, 838.0, 857.0, 134.0, 857.0], "score": 0.98, "text": "disparities along object boundaries. In fact, Yoon and Kweon's"}, {"category_id": 15, "poly": [134.0, 859.0, 838.0, 859.0, 838.0, 891.0, 134.0, 891.0], "score": 1.0, "text": "adaptive support-weight cost aggregation scheme is equivalent"}, {"category_id": 15, "poly": [132.0, 891.0, 838.0, 891.0, 838.0, 924.0, 132.0, 924.0], "score": 0.98, "text": "to the application of the so-called joint bilateral filter to the"}, {"category_id": 15, "poly": [134.0, 924.0, 547.0, 924.0, 547.0, 956.0, 134.0, 956.0], "score": 1.0, "text": "layers of the matching cost volume."}, {"category_id": 15, "poly": [889.0, 422.0, 1568.0, 424.0, 1568.0, 456.0, 889.0, 454.0], "score": 0.98, "text": "The proposed temporal stereo matching algorithm is an"}, {"category_id": 15, "poly": [862.0, 456.0, 1571.0, 456.0, 1571.0, 495.0, 862.0, 495.0], "score": 1.0, "text": "extension of the real-time iterative adaptive support-weight"}, {"category_id": 15, "poly": [864.0, 490.0, 1568.0, 490.0, 1568.0, 522.0, 864.0, 522.0], "score": 0.99, "text": "algorithm described in [3]. In addition to real-time two-"}, {"category_id": 15, "poly": [864.0, 525.0, 1566.0, 525.0, 1566.0, 557.0, 864.0, 557.0], "score": 1.0, "text": "pass aggregation of the cost values in the spatial domain,"}, {"category_id": 15, "poly": [864.0, 557.0, 1568.0, 557.0, 1568.0, 589.0, 864.0, 589.0], "score": 0.99, "text": "the proposed algorithm enhances stereo matching on video"}, {"category_id": 15, "poly": [866.0, 594.0, 1566.0, 594.0, 1566.0, 626.0, 866.0, 626.0], "score": 0.97, "text": "sequences by aggregating costs along the time dimension."}, {"category_id": 15, "poly": [864.0, 626.0, 1568.0, 626.0, 1568.0, 658.0, 864.0, 658.0], "score": 1.0, "text": "The operation of the algorithm has been divided into four"}, {"category_id": 15, "poly": [866.0, 660.0, 1568.0, 660.0, 1568.0, 692.0, 866.0, 692.0], "score": 0.99, "text": "stages: 1) two-pass spatial cost aggregation, 2) temporal cost"}, {"category_id": 15, "poly": [862.0, 688.0, 1568.0, 685.0, 1568.0, 724.0, 862.0, 727.0], "score": 1.0, "text": "aggregation, 3) disparity selection and confidence assessment,"}, {"category_id": 15, "poly": [866.0, 724.0, 1568.0, 724.0, 1568.0, 756.0, 866.0, 756.0], "score": 1.0, "text": "and 4) iterative disparity refinement. In the following, each of"}, {"category_id": 15, "poly": [864.0, 759.0, 1254.0, 759.0, 1254.0, 791.0, 864.0, 791.0], "score": 1.0, "text": "these stages is described in detail."}, {"category_id": 15, "poly": [860.0, 1265.0, 1194.0, 1270.0, 1194.0, 1306.0, 859.0, 1301.0], "score": 0.99, "text": " color similarity, respectively."}, {"category_id": 15, "poly": [1433.0, 1169.0, 1566.0, 1169.0, 1566.0, 1201.0, 1433.0, 1201.0], "score": 0.98, "text": "is the color"}, {"category_id": 15, "poly": [864.0, 1169.0, 938.0, 1169.0, 938.0, 1201.0, 864.0, 1201.0], "score": 1.0, "text": "where"}, {"category_id": 15, "poly": [1040.0, 1169.0, 1334.0, 1169.0, 1334.0, 1201.0, 1040.0, 1201.0], "score": 0.98, "text": "is the geometric distance,"}, {"category_id": 15, "poly": [1517.0, 1196.0, 1566.0, 1201.0, 1566.0, 1240.0, 1517.0, 1235.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [862.0, 1196.0, 1158.0, 1201.0, 1158.0, 1240.0, 861.0, 1235.0], "score": 1.0, "text": "difference between pixels"}, {"category_id": 15, "poly": [894.0, 1233.0, 1566.0, 1231.0, 1566.0, 1270.0, 894.0, 1272.0], "score": 0.97, "text": "regulate the strength of grouping by geometric distance and"}, {"category_id": 15, "poly": [1179.0, 1196.0, 1229.0, 1201.0, 1229.0, 1240.0, 1179.0, 1235.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [1248.0, 1196.0, 1484.0, 1201.0, 1484.0, 1240.0, 1248.0, 1235.0], "score": 0.99, "text": ", and the coefficients"}, {"category_id": 15, "poly": [887.0, 848.0, 1568.0, 850.0, 1568.0, 889.0, 887.0, 887.0], "score": 0.99, "text": " Humans group shapes by observing the geometric distance"}, {"category_id": 15, "poly": [859.0, 885.0, 1568.0, 882.0, 1568.0, 921.0, 859.0, 924.0], "score": 0.98, "text": " and color similarity of points in space. To mimic this vi-"}, {"category_id": 15, "poly": [864.0, 921.0, 1568.0, 921.0, 1568.0, 953.0, 864.0, 953.0], "score": 0.99, "text": "sual grouping, the adaptive support-weight stereo matching"}, {"category_id": 15, "poly": [864.0, 1054.0, 899.0, 1054.0, 899.0, 1084.0, 864.0, 1084.0], "score": 1.0, "text": "by"}, {"category_id": 15, "poly": [866.0, 956.0, 1350.0, 956.0, 1350.0, 988.0, 866.0, 988.0], "score": 0.98, "text": "algorithm [4] considers a support window"}, {"category_id": 15, "poly": [1389.0, 956.0, 1566.0, 956.0, 1566.0, 988.0, 1389.0, 988.0], "score": 0.98, "text": " centered at the"}, {"category_id": 15, "poly": [952.0, 1022.0, 1370.0, 1022.0, 1370.0, 1054.0, 952.0, 1054.0], "score": 0.98, "text": ". The support weight relating pixels"}, {"category_id": 15, "poly": [1392.0, 1022.0, 1446.0, 1022.0, 1446.0, 1054.0, 1392.0, 1054.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [1466.0, 1022.0, 1566.0, 1022.0, 1566.0, 1054.0, 1466.0, 1054.0], "score": 0.98, "text": "is given"}, {"category_id": 15, "poly": [866.0, 990.0, 1049.0, 990.0, 1049.0, 1022.0, 866.0, 1022.0], "score": 1.0, "text": "pixel of interest"}, {"category_id": 15, "poly": [1069.0, 990.0, 1566.0, 990.0, 1566.0, 1022.0, 1069.0, 1022.0], "score": 1.0, "text": ", and assigns a support weight to each pixel"}, {"category_id": 15, "poly": [862.0, 1948.0, 1568.0, 1950.0, 1568.0, 1989.0, 861.0, 1987.0], "score": 0.98, "text": "vides additional robustness to outliers. Rather than evaluating"}, {"category_id": 15, "poly": [864.0, 1989.0, 1566.0, 1989.0, 1566.0, 2021.0, 864.0, 2021.0], "score": 0.98, "text": "Equation (2) directly, real-time algorithms often approximate"}, {"category_id": 15, "poly": [862.0, 1920.0, 1406.0, 1920.0, 1406.0, 1952.0, 862.0, 1952.0], "score": 0.99, "text": "This limits each of their magnitudes to at most"}, {"category_id": 15, "poly": [1426.0, 1920.0, 1561.0, 1920.0, 1561.0, 1952.0, 1426.0, 1952.0], "score": 0.96, "text": ",whichpro-"}, {"category_id": 15, "poly": [859.0, 1331.0, 1571.0, 1334.0, 1571.0, 1373.0, 859.0, 1370.0], "score": 0.98, "text": " iterative adaptive support-weight algorithm evaluates matching"}, {"category_id": 15, "poly": [859.0, 1464.0, 912.0, 1467.0, 912.0, 1506.0, 859.0, 1503.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [950.0, 1464.0, 1474.0, 1467.0, 1474.0, 1506.0, 950.0, 1503.0], "score": 1.0, "text": ", the initial matching cost is aggregated using"}, {"category_id": 15, "poly": [1442.0, 1370.0, 1530.0, 1370.0, 1530.0, 1402.0, 1442.0, 1402.0], "score": 0.98, "text": ", where"}, {"category_id": 15, "poly": [1197.0, 1437.0, 1527.0, 1437.0, 1527.0, 1469.0, 1197.0, 1469.0], "score": 0.97, "text": ", and their support windows"}, {"category_id": 15, "poly": [866.0, 1402.0, 1539.0, 1402.0, 1539.0, 1435.0, 866.0, 1435.0], "score": 1.0, "text": "denotes a set of matching candidates associated with pixel"}, {"category_id": 15, "poly": [864.0, 1437.0, 1100.0, 1437.0, 1100.0, 1469.0, 864.0, 1469.0], "score": 0.97, "text": "For a pair of pixels"}, {"category_id": 15, "poly": [1122.0, 1437.0, 1176.0, 1437.0, 1176.0, 1469.0, 1122.0, 1469.0], "score": 0.94, "text": " and"}, {"category_id": 15, "poly": [887.0, 1299.0, 1388.0, 1304.0, 1388.0, 1336.0, 887.0, 1331.0], "score": 0.96, "text": " To identify a match for the pixel of interest"}, {"category_id": 15, "poly": [1408.0, 1299.0, 1568.0, 1304.0, 1568.0, 1336.0, 1408.0, 1331.0], "score": 1.0, "text": ", the real-time"}, {"category_id": 15, "poly": [864.0, 1370.0, 1028.0, 1370.0, 1028.0, 1402.0, 864.0, 1402.0], "score": 1.0, "text": "costs between"}, {"category_id": 15, "poly": [1049.0, 1370.0, 1361.0, 1370.0, 1361.0, 1402.0, 1049.0, 1402.0], "score": 0.99, "text": " and every match candidate"}, {"category_id": 15, "poly": [160.0, 1618.0, 836.0, 1623.0, 836.0, 1655.0, 159.0, 1650.0], "score": 0.99, "text": "Most recently, local stereo algorithms based on edge-aware"}, {"category_id": 15, "poly": [127.0, 1650.0, 841.0, 1652.0, 841.0, 1691.0, 127.0, 1689.0], "score": 0.97, "text": " filters were extended to incorporate temporal evidence into"}, {"category_id": 15, "poly": [132.0, 1687.0, 836.0, 1687.0, 836.0, 1719.0, 132.0, 1719.0], "score": 0.97, "text": "the matching process. The method of Richardt et al. [19]"}, {"category_id": 15, "poly": [134.0, 1723.0, 838.0, 1723.0, 838.0, 1753.0, 134.0, 1753.0], "score": 0.99, "text": "employs a variant of the bilateral grid [20] implemented on"}, {"category_id": 15, "poly": [134.0, 1755.0, 838.0, 1755.0, 838.0, 1788.0, 134.0, 1788.0], "score": 0.99, "text": "graphics hardware, which accelerates cost aggregation and"}, {"category_id": 15, "poly": [134.0, 1788.0, 838.0, 1788.0, 838.0, 1820.0, 134.0, 1820.0], "score": 1.0, "text": "allows for weighted propagation of pixel dissimilarity metrics"}, {"category_id": 15, "poly": [132.0, 1822.0, 838.0, 1822.0, 838.0, 1854.0, 132.0, 1854.0], "score": 0.99, "text": "from previous frames to the current one. Although this method"}, {"category_id": 15, "poly": [129.0, 1856.0, 838.0, 1856.0, 838.0, 1888.0, 129.0, 1888.0], "score": 1.0, "text": " outperforms the baseline frame-to-frame approach, the amount"}, {"category_id": 15, "poly": [132.0, 1888.0, 838.0, 1888.0, 838.0, 1920.0, 132.0, 1920.0], "score": 0.97, "text": "of hardware memory necessary to construct the bilateral grid"}, {"category_id": 15, "poly": [127.0, 1916.0, 841.0, 1918.0, 841.0, 1957.0, 127.0, 1955.0], "score": 0.99, "text": "limits its application to single-channel, i.e., grayscale images "}, {"category_id": 15, "poly": [132.0, 1955.0, 838.0, 1955.0, 838.0, 1985.0, 132.0, 1985.0], "score": 0.99, "text": "only. Hosni et al. [10], on the other hand, reformulated kernels"}, {"category_id": 15, "poly": [132.0, 1989.0, 838.0, 1989.0, 838.0, 2021.0, 132.0, 2021.0], "score": 0.99, "text": "of the guided image filter to operate on both spatial and"}, {"category_id": 15, "poly": [859.0, 809.0, 1307.0, 809.0, 1307.0, 848.0, 859.0, 848.0], "score": 0.99, "text": "A. Two-Pass Spatial Cost Aggregation"}, {"category_id": 15, "poly": [1129.0, 376.0, 1300.0, 376.0, 1300.0, 417.0, 1129.0, 417.0], "score": 0.94, "text": "III. 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"poly": [581, 1713, 694, 1713, 694, 1747, 581, 1747], "score": 0.93, "latex": "{\\bar{p}}=m(p)"}, {"category_id": 14, "poly": [947, 373, 1478, 373, 1478, 454, 947, 454], "score": 0.93, "latex": "\\Lambda^{i}(p,\\bar{p})=\\alpha\\times\\sum_{q\\in\\Omega_{p}}w(p,q)F_{q}^{i-1}\\left|D_{q}^{i-1}-d_{p}\\right|\\,,"}, {"category_id": 13, "poly": [426, 445, 512, 445, 512, 479, 426, 479], "score": 0.93, "latex": "C(p,{\\bar{p}})"}, {"category_id": 13, "poly": [337, 356, 414, 356, 414, 391, 337, 391], "score": 0.93, "latex": "\\mathcal{O}(\\omega^{2})"}, {"category_id": 13, "poly": [1341, 730, 1565, 730, 1565, 765, 1341, 765], "score": 0.92, "latex": "C_{a}(p,\\bar{p})\\gets C(p,\\bar{p})"}, {"category_id": 13, "poly": [629, 1436, 691, 1436, 691, 1470, 629, 1470], "score": 0.92, "latex": "m(p)"}, {"category_id": 13, "poly": [277, 1469, 361, 1469, 361, 1504, 277, 1504], "score": 0.92, "latex": "\\bar{p}\\in S_{p}"}, {"category_id": 14, "poly": [1030, 541, 1398, 541, 1398, 582, 1030, 582], "score": 0.92, "latex": "C^{i}(p,\\bar{p})=C^{0}(p,\\bar{p})+{\\Lambda^{i}}(p,\\bar{p})\\,,"}, {"category_id": 13, "poly": [453, 356, 518, 356, 518, 391, 453, 391], "score": 0.91, "latex": "\\mathcal{O}(\\omega)"}, {"category_id": 14, "poly": [146, 714, 787, 714, 787, 791, 146, 791], "score": 0.91, "latex": "C(p,\\bar{p})\\gets\\frac{(1-\\lambda)\\cdot C(p,\\bar{p})+\\lambda\\cdot w_{t}(p,p_{t-1})\\cdot C_{a}(p,\\bar{p})}{(1-\\lambda)+\\lambda\\cdot w_{t}(p,p_{t-1})},"}, {"category_id": 13, "poly": [1095, 231, 1134, 231, 1134, 270, 1095, 270], "score": 0.9, "latex": "D_{p}^{i}"}, {"category_id": 13, "poly": [1313, 1752, 1447, 1752, 1447, 1783, 1313, 1783], "score": 0.89, "latex": "640~\\times~480"}, {"category_id": 13, "poly": [593, 1782, 627, 1782, 627, 1815, 593, 1815], "score": 0.89, "latex": "F_{p}"}, {"category_id": 13, "poly": [133, 326, 209, 326, 209, 355, 133, 355], "score": 0.88, "latex": "\\omega\\times\\omega"}, {"category_id": 13, "poly": [208, 1089, 236, 1089, 236, 1116, 208, 1116], "score": 0.85, "latex": "\\gamma_{t}"}, {"category_id": 13, "poly": [1466, 769, 1484, 769, 1484, 797, 1466, 797], "score": 0.83, "latex": "\\bar{p}"}, {"category_id": 13, "poly": [133, 935, 177, 935, 177, 963, 133, 963], "score": 0.83, "latex": "p_{t-1}"}, {"category_id": 13, "poly": [608, 1753, 627, 1753, 627, 1779, 608, 1779], "score": 0.81, "latex": "p"}, {"category_id": 13, "poly": [491, 799, 511, 799, 511, 825, 491, 825], "score": 0.81, "latex": "\\lambda"}, {"category_id": 13, "poly": [1018, 770, 1037, 770, 1037, 796, 1018, 796], "score": 0.81, "latex": "p"}, {"category_id": 13, "poly": [1086, 470, 1107, 470, 1107, 491, 1086, 491], "score": 0.8, "latex": "\\alpha"}, {"category_id": 13, "poly": [466, 901, 485, 901, 485, 929, 466, 929], "score": 0.8, "latex": "p"}, {"category_id": 13, "poly": [208, 484, 227, 484, 227, 511, 208, 511], "score": 0.79, "latex": "p"}, {"category_id": 13, "poly": [462, 1443, 480, 1443, 480, 1468, 462, 1468], "score": 0.77, "latex": "p"}, {"category_id": 13, "poly": [266, 514, 288, 514, 288, 544, 266, 544], "score": 0.77, "latex": "\\bar{p}"}, {"category_id": 13, "poly": [816, 1716, 836, 1716, 836, 1746, 816, 1746], "score": 0.73, "latex": "\\bar{p}"}, {"category_id": 13, "poly": [132, 405, 154, 405, 154, 432, 132, 432], "score": 0.27, "latex": "B"}, {"category_id": 13, "poly": [862, 160, 887, 160, 887, 187, 862, 187], "score": 0.26, "latex": "D"}, {"category_id": 15, "poly": [887.0, 852.0, 1568.0, 855.0, 1568.0, 894.0, 887.0, 891.0], "score": 0.98, "text": " The speed and accuracy of real-time stereo matching al-"}, {"category_id": 15, "poly": [864.0, 891.0, 1566.0, 891.0, 1566.0, 924.0, 864.0, 924.0], "score": 0.99, "text": "gorithms are traditionally demonstrated using still-frame im-"}, {"category_id": 15, "poly": [859.0, 921.0, 1571.0, 919.0, 1571.0, 958.0, 859.0, 960.0], "score": 0.97, "text": " ages from the Middlebury stereo benchmark [1], [2]. Still"}, {"category_id": 15, "poly": [862.0, 956.0, 1568.0, 958.0, 1568.0, 990.0, 862.0, 988.0], "score": 0.99, "text": "frames, however, are insufficient for evaluating stereo match-"}, {"category_id": 15, "poly": [864.0, 992.0, 1571.0, 992.0, 1571.0, 1024.0, 864.0, 1024.0], "score": 1.0, "text": "ing algorithms that incorporate frame-to-frame prediction to"}, {"category_id": 15, "poly": [864.0, 1027.0, 1568.0, 1027.0, 1568.0, 1059.0, 864.0, 1059.0], "score": 0.97, "text": "enhance matching accuracy. An alternative approach is to"}, {"category_id": 15, "poly": [864.0, 1059.0, 1566.0, 1059.0, 1566.0, 1089.0, 864.0, 1089.0], "score": 0.99, "text": "use a stereo video sequence with a ground truth disparity"}, {"category_id": 15, "poly": [862.0, 1091.0, 1566.0, 1091.0, 1566.0, 1123.0, 862.0, 1123.0], "score": 1.0, "text": "for each frame. Obtaining the ground truth disparity of real"}, {"category_id": 15, "poly": [866.0, 1125.0, 1566.0, 1125.0, 1566.0, 1157.0, 866.0, 1157.0], "score": 0.98, "text": "world video sequences is a difficult undertaking due to the"}, {"category_id": 15, "poly": [859.0, 1153.0, 1568.0, 1155.0, 1568.0, 1194.0, 859.0, 1192.0], "score": 0.99, "text": "high frame rate of video and limitations in depth sensing-"}, {"category_id": 15, "poly": [864.0, 1192.0, 1568.0, 1192.0, 1568.0, 1224.0, 864.0, 1224.0], "score": 0.99, "text": "technology. To address the need for stereo video with ground"}, {"category_id": 15, "poly": [864.0, 1224.0, 1568.0, 1224.0, 1568.0, 1256.0, 864.0, 1256.0], "score": 0.99, "text": "truth disparities, five pairs of synthetic stereo video sequences"}, {"category_id": 15, "poly": [864.0, 1258.0, 1568.0, 1258.0, 1568.0, 1290.0, 864.0, 1290.0], "score": 0.99, "text": "of a computer-generated scene were given in [19]. While these"}, {"category_id": 15, "poly": [864.0, 1290.0, 1566.0, 1290.0, 1566.0, 1322.0, 864.0, 1322.0], "score": 1.0, "text": "videos incorporate a sufficient amount of movement variation,"}, {"category_id": 15, "poly": [862.0, 1325.0, 1568.0, 1325.0, 1568.0, 1357.0, 862.0, 1357.0], "score": 0.99, "text": "they were generated from relatively simple models using low-"}, {"category_id": 15, "poly": [862.0, 1359.0, 1571.0, 1359.0, 1571.0, 1389.0, 862.0, 1389.0], "score": 0.99, "text": "resolution rendering, and they do not provide occlusion or"}, {"category_id": 15, "poly": [862.0, 1386.0, 1088.0, 1394.0, 1087.0, 1426.0, 861.0, 1418.0], "score": 0.98, "text": "discontinuity maps."}, {"category_id": 15, "poly": [129.0, 156.0, 839.0, 158.0, 838.0, 197.0, 129.0, 195.0], "score": 0.99, "text": "the matching cost by performing two-pass aggregation using"}, {"category_id": 15, "poly": [130.0, 188.0, 841.0, 193.0, 841.0, 229.0, 129.0, 225.0], "score": 0.98, "text": "two orthogonal 1D windows [5], [6], [8]. The two-pass method "}, {"category_id": 15, "poly": [129.0, 225.0, 841.0, 222.0, 841.0, 261.0, 129.0, 264.0], "score": 0.99, "text": "first aggregates matching costs in the vertical direction, and"}, {"category_id": 15, "poly": [134.0, 261.0, 838.0, 261.0, 838.0, 293.0, 134.0, 293.0], "score": 0.99, "text": "then computes a weighted sum of the aggregated costs in the"}, {"category_id": 15, "poly": [132.0, 291.0, 838.0, 291.0, 838.0, 330.0, 132.0, 330.0], "score": 0.99, "text": "horizontal direction. Given that support regions are of size"}, {"category_id": 15, "poly": [136.0, 360.0, 336.0, 360.0, 336.0, 392.0, 136.0, 392.0], "score": 0.99, "text": "aggregation from"}, {"category_id": 15, "poly": [415.0, 360.0, 452.0, 360.0, 452.0, 392.0, 415.0, 392.0], "score": 0.98, "text": "to"}, {"category_id": 15, "poly": [210.0, 321.0, 836.0, 321.0, 836.0, 360.0, 210.0, 360.0], "score": 0.98, "text": ", the two-pass method reduces the complexity of cost"}, {"category_id": 15, "poly": [887.0, 1416.0, 1571.0, 1419.0, 1571.0, 1458.0, 887.0, 1455.0], "score": 0.98, "text": " To evaluate the performance of temporal aggregation, a"}, {"category_id": 15, "poly": [862.0, 1453.0, 1566.0, 1453.0, 1566.0, 1485.0, 862.0, 1485.0], "score": 0.98, "text": "new synthetic stereo video sequence is introduced along with"}, {"category_id": 15, "poly": [862.0, 1490.0, 1566.0, 1487.0, 1566.0, 1519.0, 862.0, 1522.0], "score": 0.99, "text": "corresponding disparity maps, occlusion maps, and disconti-"}, {"category_id": 15, "poly": [862.0, 1519.0, 1571.0, 1519.0, 1571.0, 1558.0, 862.0, 1558.0], "score": 0.99, "text": "nuity maps for evaluating the performance of temporal stereo"}, {"category_id": 15, "poly": [864.0, 1556.0, 1568.0, 1556.0, 1568.0, 1588.0, 864.0, 1588.0], "score": 1.0, "text": "matching algorithms. To create the video sequence, a complex"}, {"category_id": 15, "poly": [864.0, 1590.0, 1568.0, 1590.0, 1568.0, 1620.0, 864.0, 1620.0], "score": 0.99, "text": "scene was constructed using Google Sketchup and a pair"}, {"category_id": 15, "poly": [864.0, 1622.0, 1568.0, 1622.0, 1568.0, 1655.0, 864.0, 1655.0], "score": 0.99, "text": "of animated paths were rendered photorealistically using the"}, {"category_id": 15, "poly": [859.0, 1650.0, 1571.0, 1652.0, 1571.0, 1691.0, 859.0, 1689.0], "score": 0.99, "text": " Kerkythea rendering software. Realistic material properties"}, {"category_id": 15, "poly": [864.0, 1689.0, 1566.0, 1689.0, 1566.0, 1721.0, 864.0, 1721.0], "score": 1.0, "text": "were used to give surfaces a natural-looking appearance by"}, {"category_id": 15, "poly": [864.0, 1723.0, 1566.0, 1723.0, 1566.0, 1755.0, 864.0, 1755.0], "score": 0.98, "text": "adjusting their specularity, reflectance, and diffusion. The"}, {"category_id": 15, "poly": [864.0, 1788.0, 1568.0, 1788.0, 1568.0, 1820.0, 864.0, 1820.0], "score": 1.0, "text": "frame rate of 30 frames per second, and a duration of 4"}, {"category_id": 15, "poly": [862.0, 1817.0, 1568.0, 1820.0, 1568.0, 1859.0, 861.0, 1856.0], "score": 0.98, "text": "seconds. In addition to performing photorealistic rendering."}, {"category_id": 15, "poly": [864.0, 1856.0, 1568.0, 1856.0, 1568.0, 1888.0, 864.0, 1888.0], "score": 0.99, "text": "depth renders of both video sequences were also generated and"}, {"category_id": 15, "poly": [864.0, 1888.0, 1566.0, 1888.0, 1566.0, 1920.0, 864.0, 1920.0], "score": 0.98, "text": "converted to ground truth disparity for the stereo video. The"}, {"category_id": 15, "poly": [862.0, 1920.0, 1564.0, 1920.0, 1564.0, 1952.0, 862.0, 1952.0], "score": 0.99, "text": "video sequences and ground truth data have been made avail-"}, {"category_id": 15, "poly": [862.0, 1950.0, 1566.0, 1953.0, 1566.0, 1985.0, 862.0, 1982.0], "score": 0.99, "text": "able at http://mc2.unl.edu/current-research"}, {"category_id": 15, "poly": [866.0, 1989.0, 1566.0, 1989.0, 1566.0, 2019.0, 866.0, 2019.0], "score": 0.98, "text": "/ image-processing/. Figure 2 shows two sample frames"}, {"category_id": 15, "poly": [862.0, 1755.0, 1312.0, 1755.0, 1312.0, 1788.0, 862.0, 1788.0], "score": 0.97, "text": "video sequence has a resolution of "}, {"category_id": 15, "poly": [1448.0, 1755.0, 1566.0, 1755.0, 1566.0, 1788.0, 1448.0, 1788.0], "score": 0.99, "text": "pixels,a"}, {"category_id": 15, "poly": [889.0, 197.0, 1566.0, 199.0, 1566.0, 238.0, 889.0, 236.0], "score": 1.0, "text": "Once the first iteration of stereo matching is complete,"}, {"category_id": 15, "poly": [864.0, 268.0, 1566.0, 268.0, 1566.0, 300.0, 864.0, 300.0], "score": 0.99, "text": "subsequent iterations. This is done by penalizing disparities"}, {"category_id": 15, "poly": [864.0, 302.0, 1568.0, 302.0, 1568.0, 335.0, 864.0, 335.0], "score": 1.0, "text": "that deviate from their expected values. The penalty function"}, {"category_id": 15, "poly": [862.0, 337.0, 996.0, 337.0, 996.0, 369.0, 862.0, 369.0], "score": 0.97, "text": "is given by"}, {"category_id": 15, "poly": [864.0, 236.0, 1094.0, 236.0, 1094.0, 268.0, 864.0, 268.0], "score": 0.96, "text": "disparityestimates"}, {"category_id": 15, "poly": [1135.0, 236.0, 1568.0, 236.0, 1568.0, 268.0, 1135.0, 268.0], "score": 0.97, "text": " can be used to guide matching in"}, {"category_id": 15, "poly": [157.0, 1366.0, 839.0, 1368.0, 838.0, 1407.0, 157.0, 1405.0], "score": 1.0, "text": "Having performed temporal cost aggregation, matches are"}, {"category_id": 15, "poly": [134.0, 1405.0, 834.0, 1405.0, 834.0, 1437.0, 134.0, 1437.0], "score": 0.99, "text": "determined using the Winner-Takes-All (WTA) match selec-"}, {"category_id": 15, "poly": [132.0, 1506.0, 374.0, 1506.0, 374.0, 1538.0, 132.0, 1538.0], "score": 1.0, "text": "cost, and is given by"}, {"category_id": 15, "poly": [692.0, 1439.0, 834.0, 1439.0, 834.0, 1471.0, 692.0, 1471.0], "score": 0.99, "text": ", is the can-"}, {"category_id": 15, "poly": [134.0, 1474.0, 276.0, 1474.0, 276.0, 1506.0, 134.0, 1506.0], "score": 0.98, "text": "didate pixel"}, {"category_id": 15, "poly": [362.0, 1474.0, 836.0, 1474.0, 836.0, 1506.0, 362.0, 1506.0], "score": 0.99, "text": " characterized by the minimum matching"}, {"category_id": 15, "poly": [134.0, 1439.0, 461.0, 1439.0, 461.0, 1471.0, 134.0, 1471.0], "score": 1.0, "text": "tion criteria. The match for"}, {"category_id": 15, "poly": [481.0, 1439.0, 628.0, 1439.0, 628.0, 1471.0, 481.0, 1471.0], "score": 0.96, "text": ", denoted as"}, {"category_id": 15, "poly": [134.0, 548.0, 838.0, 545.0, 838.0, 577.0, 134.0, 580.0], "score": 0.99, "text": "aggregation routine is exectuted. At each time instance, the"}, {"category_id": 15, "poly": [134.0, 614.0, 834.0, 614.0, 834.0, 646.0, 134.0, 646.0], "score": 1.0, "text": "weighted summation of costs obtained in the previous frames."}, {"category_id": 15, "poly": [132.0, 646.0, 838.0, 644.0, 838.0, 676.0, 132.0, 678.0], "score": 1.0, "text": "During temporal aggregation, the auxiliary cost is merged with"}, {"category_id": 15, "poly": [132.0, 678.0, 675.0, 681.0, 674.0, 713.0, 132.0, 710.0], "score": 0.99, "text": "the cost obtained from the current frame using"}, {"category_id": 15, "poly": [134.0, 580.0, 549.0, 580.0, 549.0, 612.0, 134.0, 612.0], "score": 1.0, "text": "algorithm stores an auxiliary cost"}, {"category_id": 15, "poly": [649.0, 580.0, 841.0, 580.0, 841.0, 612.0, 649.0, 612.0], "score": 0.96, "text": "which  holds  a"}, {"category_id": 15, "poly": [157.0, 445.0, 425.0, 442.0, 425.0, 481.0, 157.0, 484.0], "score": 0.98, "text": " Once aggregated costs"}, {"category_id": 15, "poly": [513.0, 445.0, 838.0, 442.0, 838.0, 481.0, 513.0, 484.0], "score": 0.96, "text": " have been computed for all"}, {"category_id": 15, "poly": [132.0, 481.0, 207.0, 481.0, 207.0, 513.0, 132.0, 513.0], "score": 1.0, "text": "pixels"}, {"category_id": 15, "poly": [228.0, 481.0, 838.0, 481.0, 838.0, 513.0, 228.0, 513.0], "score": 0.97, "text": " in the reference image and their respective matching"}, {"category_id": 15, "poly": [134.0, 516.0, 265.0, 516.0, 265.0, 548.0, 134.0, 548.0], "score": 1.0, "text": "candidates"}, {"category_id": 15, "poly": [289.0, 516.0, 838.0, 516.0, 838.0, 548.0, 289.0, 548.0], "score": 0.98, "text": " in the target image, a single-pass temporal"}, {"category_id": 15, "poly": [132.0, 1116.0, 841.0, 1116.0, 841.0, 1155.0, 132.0, 1155.0], "score": 0.99, "text": "in the temporal dimension. The temporal adaptive weight has "}, {"category_id": 15, "poly": [134.0, 1153.0, 838.0, 1153.0, 838.0, 1185.0, 134.0, 1185.0], "score": 0.99, "text": "the effect of preserving edges in the temporal domain, such"}, {"category_id": 15, "poly": [132.0, 1182.0, 836.0, 1182.0, 836.0, 1215.0, 132.0, 1215.0], "score": 0.98, "text": "that when a pixel coordinate transitions from one side of an"}, {"category_id": 15, "poly": [134.0, 1219.0, 838.0, 1219.0, 838.0, 1251.0, 134.0, 1251.0], "score": 0.98, "text": "edge to another in subsequent frames, the auxiliary cost is"}, {"category_id": 15, "poly": [134.0, 1254.0, 838.0, 1254.0, 838.0, 1283.0, 134.0, 1283.0], "score": 0.99, "text": "assigned a small weight and the majority of the cost is derived"}, {"category_id": 15, "poly": [130.0, 1283.0, 404.0, 1286.0, 404.0, 1318.0, 129.0, 1315.0], "score": 1.0, "text": "from the current frame."}, {"category_id": 15, "poly": [134.0, 1086.0, 207.0, 1086.0, 207.0, 1118.0, 134.0, 1118.0], "score": 0.99, "text": "where"}, {"category_id": 15, "poly": [237.0, 1086.0, 836.0, 1086.0, 836.0, 1118.0, 237.0, 1118.0], "score": 0.99, "text": "regulates the strength of grouping by color similarity"}, {"category_id": 15, "poly": [864.0, 600.0, 1568.0, 600.0, 1568.0, 632.0, 864.0, 632.0], "score": 1.0, "text": "and the matches are reselected using the WTA match selection"}, {"category_id": 15, "poly": [864.0, 635.0, 1568.0, 635.0, 1568.0, 667.0, 864.0, 667.0], "score": 0.99, "text": "criteria. 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RESULTS"}, {"category_id": 15, "poly": [155.0, 401.0, 481.0, 406.0, 480.0, 445.0, 155.0, 440.0], "score": 0.99, "text": "Temporal cost aggregation"}, {"category_id": 15, "poly": [129.0, 1325.0, 718.0, 1327.0, 718.0, 1366.0, 129.0, 1363.0], "score": 0.99, "text": "C. 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results of temporal stereo matching are given in Figure"}, {"category_id": 15, "poly": [132.0, 1657.0, 838.0, 1657.0, 838.0, 1689.0, 132.0, 1689.0], "score": 0.99, "text": "stereo matching methods, improvements are negligible when"}, {"category_id": 15, "poly": [132.0, 1691.0, 838.0, 1691.0, 838.0, 1723.0, 132.0, 1723.0], "score": 0.99, "text": "no noise is added to the images [10], [19]. 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Each performance plot is given as a function"}, {"category_id": 15, "poly": [127.0, 1551.0, 786.0, 1554.0, 786.0, 1593.0, 127.0, 1590.0], "score": 0.98, "text": " 3 for uniform additive noise confined to the ranges of"}, {"category_id": 15, "poly": [134.0, 1622.0, 435.0, 1622.0, 435.0, 1655.0, 134.0, 1655.0], "score": 0.99, "text": "of the feedback coefficient"}, {"category_id": 15, "poly": [456.0, 1622.0, 836.0, 1622.0, 836.0, 1655.0, 456.0, 1655.0], "score": 0.97, "text": ". As with the majority of temporal"}, {"category_id": 15, "poly": [134.0, 1359.0, 834.0, 1359.0, 834.0, 1391.0, 134.0, 1391.0], "score": 0.99, "text": "Figure 2: Two sample frames from the synthetic video se-"}, {"category_id": 15, "poly": [573.0, 1418.0, 836.0, 1421.0, 836.0, 1460.0, 573.0, 1457.0], "score": 1.0, "text": "row), and discontinuity"}, {"category_id": 15, "poly": [134.0, 1393.0, 229.0, 1393.0, 229.0, 1425.0, 134.0, 1425.0], "score": 0.96, "text": "quence ("}, {"category_id": 15, "poly": [267.0, 1393.0, 836.0, 1393.0, 836.0, 1425.0, 267.0, 1425.0], "score": 0.98, "text": "row), along with their corresponding ground truth"}, {"category_id": 15, "poly": [127.0, 1456.0, 199.0, 1450.0, 199.0, 1489.0, 128.0, 1495.0], "score": 0.91, "text": "map ("}, {"category_id": 15, "poly": [241.0, 1456.0, 309.0, 1450.0, 310.0, 1489.0, 241.0, 1495.0], "score": 1.0, "text": "row)."}, {"category_id": 15, "poly": [129.0, 1418.0, 245.0, 1421.0, 245.0, 1460.0, 129.0, 1457.0], "score": 0.93, "text": " disparity "}, {"category_id": 15, "poly": [290.0, 1418.0, 531.0, 1421.0, 531.0, 1460.0, 290.0, 1457.0], "score": 1.0, "text": "row), occlusion map ("}, {"category_id": 15, "poly": [159.0, 1888.0, 836.0, 1888.0, 836.0, 1920.0, 159.0, 1920.0], "score": 0.99, "text": " Significant improvements in accuracy can be seen in Figure"}, {"category_id": 15, "poly": [132.0, 1950.0, 839.0, 1955.0, 838.0, 1987.0, 132.0, 1982.0], "score": 1.0, "text": "the effect of noise in the current frame is reduced by increasing"}, {"category_id": 15, "poly": [134.0, 1920.0, 480.0, 1920.0, 480.0, 1952.0, 134.0, 1952.0], "score": 0.99, "text": "3 when the noise has ranges of"}, {"category_id": 15, "poly": [535.0, 1920.0, 590.0, 1920.0, 590.0, 1952.0, 535.0, 1952.0], "score": 0.92, "text": " and"}, {"category_id": 15, "poly": [645.0, 1920.0, 836.0, 1920.0, 836.0, 1952.0, 645.0, 1952.0], "score": 0.96, "text": ". In this scenario,"}, {"category_id": 15, "poly": [676.0, 1989.0, 838.0, 1989.0, 838.0, 2019.0, 676.0, 2019.0], "score": 0.98, "text": "has the effect"}, {"category_id": 15, "poly": [134.0, 1989.0, 409.0, 1989.0, 409.0, 2019.0, 134.0, 2019.0], "score": 1.0, "text": "the feedback coefficient"}, {"category_id": 15, "poly": [431.0, 1989.0, 654.0, 1989.0, 654.0, 2019.0, 431.0, 2019.0], "score": 0.97, "text": ". This increasing of"}, {"category_id": 15, "poly": [864.0, 1920.0, 1566.0, 1920.0, 1566.0, 1952.0, 864.0, 1952.0], "score": 0.98, "text": "of averaging out noise in the per-pixel costs by selecting"}, {"category_id": 15, "poly": [861.0, 1950.0, 1566.0, 1948.0, 1566.0, 1987.0, 862.0, 1989.0], "score": 0.98, "text": "matches based more heavily upon the auxiliary cost, which"}, {"category_id": 15, "poly": [862.0, 1989.0, 1568.0, 1989.0, 1568.0, 2021.0, 862.0, 2021.0], "score": 0.99, "text": "is essentially a much more stable running average of the cost"}, {"category_id": 15, "poly": [864.0, 1788.0, 1564.0, 1785.0, 1564.0, 1817.0, 864.0, 1820.0], "score": 0.99, "text": "responding to the smallest mean squared error (MSE) of the"}, {"category_id": 15, "poly": [864.0, 1822.0, 1427.0, 1822.0, 1427.0, 1854.0, 864.0, 1854.0], "score": 0.99, "text": "disparity estimates for a range of noise strengths."}, {"category_id": 15, "poly": [862.0, 1748.0, 1488.0, 1753.0, 1488.0, 1785.0, 861.0, 1781.0], "score": 0.99, "text": "Figure 4: Optimal values of the feedback coefficient "}, {"category_id": 15, "poly": [1511.0, 1748.0, 1561.0, 1753.0, 1561.0, 1785.0, 1511.0, 1781.0], "score": 0.96, "text": "cor-"}, {"category_id": 15, "poly": [864.0, 866.0, 1566.0, 866.0, 1566.0, 898.0, 864.0, 898.0], "score": 0.99, "text": "Figure 3: Performance of temporal matching at different levels"}, {"category_id": 15, "poly": [864.0, 935.0, 1566.0, 933.0, 1566.0, 965.0, 864.0, 967.0], "score": 0.98, "text": "squared error (MSE) of disparities is plotted versus the values"}, {"category_id": 15, "poly": [864.0, 1001.0, 1492.0, 1001.0, 1492.0, 1031.0, 864.0, 1031.0], "score": 0.99, "text": "values of MSE obtained without temporal aggregation."}, {"category_id": 15, "poly": [864.0, 901.0, 1294.0, 901.0, 1294.0, 933.0, 864.0, 933.0], "score": 0.99, "text": "of uniformly distributed image noise"}, {"category_id": 15, "poly": [1484.0, 901.0, 1568.0, 901.0, 1568.0, 933.0, 1484.0, 933.0], "score": 0.99, "text": ".Mean"}, {"category_id": 15, "poly": [864.0, 967.0, 1175.0, 967.0, 1175.0, 999.0, 864.0, 999.0], "score": 0.99, "text": "of the feedback coefficient"}, {"category_id": 15, "poly": [1196.0, 967.0, 1568.0, 967.0, 1568.0, 999.0, 1196.0, 999.0], "score": 0.99, "text": ". 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259.0], "score": 0.99, "text": "evaluated per second, as shown in Table I1. It is also the second"}, {"category_id": 15, "poly": [862.0, 261.0, 1568.0, 261.0, 1568.0, 293.0, 862.0, 293.0], "score": 0.99, "text": "most accurate real-time method in terms of error rate, as"}, {"category_id": 15, "poly": [864.0, 296.0, 1564.0, 296.0, 1564.0, 325.0, 864.0, 325.0], "score": 1.0, "text": "measured using the Middlebury stereo evaluation benchmark."}, {"category_id": 15, "poly": [859.0, 323.0, 1568.0, 325.0, 1568.0, 358.0, 859.0, 355.0], "score": 0.98, "text": " It should be noted that it is difficult to establish an unbiased"}, {"category_id": 15, "poly": [862.0, 358.0, 1566.0, 358.0, 1566.0, 390.0, 862.0, 390.0], "score": 1.0, "text": "metric for speed comparisons, as the architecture, number of"}, {"category_id": 15, "poly": [866.0, 394.0, 1568.0, 394.0, 1568.0, 426.0, 866.0, 426.0], "score": 0.98, "text": "cores, and clock speed of graphics hardware used are not"}, {"category_id": 15, "poly": [862.0, 424.0, 1259.0, 429.0, 1259.0, 461.0, 861.0, 456.0], "score": 0.99, "text": "consistent across implementations."}, {"category_id": 15, "poly": [889.0, 1061.0, 1571.0, 1061.0, 1571.0, 1100.0, 889.0, 1100.0], "score": 1.0, "text": "While the majority of stereo matching algorithms focus"}, {"category_id": 15, "poly": [859.0, 1093.0, 1571.0, 1095.0, 1571.0, 1134.0, 859.0, 1132.0], "score": 0.99, "text": " on achieving high accuracy on still images, the volume of"}, {"category_id": 15, "poly": [862.0, 1130.0, 1564.0, 1130.0, 1564.0, 1162.0, 862.0, 1162.0], "score": 0.99, "text": "research aimed at recovery of temporally consistent disparity"}, {"category_id": 15, "poly": [862.0, 1162.0, 1568.0, 1162.0, 1568.0, 1201.0, 862.0, 1201.0], "score": 0.99, "text": "maps remains disproportionally small. This paper introduces"}, {"category_id": 15, "poly": [862.0, 1196.0, 1568.0, 1196.0, 1568.0, 1235.0, 862.0, 1235.0], "score": 0.98, "text": "an efficient temporal cost aggregation scheme that can easily"}, {"category_id": 15, "poly": [859.0, 1226.0, 1571.0, 1228.0, 1571.0, 1267.0, 859.0, 1265.0], "score": 0.99, "text": "be combined with conventional spatial cost aggregation to"}, {"category_id": 15, "poly": [864.0, 1265.0, 1568.0, 1265.0, 1568.0, 1297.0, 864.0, 1297.0], "score": 1.0, "text": "improve the accuracy of stereo matching when operating on"}, {"category_id": 15, "poly": [864.0, 1297.0, 1568.0, 1297.0, 1568.0, 1329.0, 864.0, 1329.0], "score": 0.99, "text": "video sequences. A synthetic video sequence, along with"}, {"category_id": 15, "poly": [864.0, 1331.0, 1568.0, 1331.0, 1568.0, 1364.0, 864.0, 1364.0], "score": 0.99, "text": "ground truth disparity data, was generated to evaluate the"}, {"category_id": 15, "poly": [862.0, 1361.0, 1571.0, 1361.0, 1571.0, 1400.0, 862.0, 1400.0], "score": 0.98, "text": "performance of the proposed method. It was shown that"}, {"category_id": 15, "poly": [864.0, 1398.0, 1571.0, 1398.0, 1571.0, 1430.0, 864.0, 1430.0], "score": 0.98, "text": "temporal aggregation is significantly more robust to noise than"}, {"category_id": 15, "poly": [862.0, 1430.0, 1497.0, 1430.0, 1497.0, 1462.0, 862.0, 1462.0], "score": 0.99, "text": "a method that only considers the current stereo frames."}, {"category_id": 15, "poly": [157.0, 1517.0, 838.0, 1517.0, 838.0, 1556.0, 157.0, 1556.0], "score": 0.99, "text": "The optimal value of the feedback coefficient is largely"}, {"category_id": 15, "poly": [134.0, 1554.0, 836.0, 1554.0, 836.0, 1584.0, 134.0, 1584.0], "score": 0.97, "text": "dependent on the noise being added to the image. Figure 4"}, {"category_id": 15, "poly": [132.0, 1655.0, 838.0, 1655.0, 838.0, 1684.0, 132.0, 1684.0], "score": 0.99, "text": "rely on the auxiliary cost when noise is high and it is more"}, {"category_id": 15, "poly": [132.0, 1684.0, 839.0, 1689.0, 838.0, 1721.0, 132.0, 1716.0], "score": 0.98, "text": "beneficial to rely on the current cost when noise is low. Figure"}, {"category_id": 15, "poly": [132.0, 1719.0, 839.0, 1723.0, 838.0, 1755.0, 132.0, 1751.0], "score": 1.0, "text": "5 illustrates the improvements that are achieved when applying"}, {"category_id": 15, "poly": [134.0, 1755.0, 836.0, 1755.0, 836.0, 1785.0, 134.0, 1785.0], "score": 0.98, "text": "temporal stereo matching to a particular pair of frames in the"}, {"category_id": 15, "poly": [134.0, 1788.0, 834.0, 1788.0, 834.0, 1820.0, 134.0, 1820.0], "score": 1.0, "text": "synthetic video sequence. Clearly, the noise in the disparity"}, {"category_id": 15, "poly": [134.0, 1822.0, 836.0, 1822.0, 836.0, 1854.0, 134.0, 1854.0], "score": 0.99, "text": "map is drastically reduced when temporal stereo matching is"}, {"category_id": 15, "poly": [132.0, 1856.0, 196.0, 1856.0, 196.0, 1886.0, 132.0, 1886.0], "score": 1.0, "text": "used."}, {"category_id": 15, "poly": [132.0, 1620.0, 165.0, 1620.0, 165.0, 1652.0, 132.0, 1652.0], "score": 0.99, "text": "to"}, {"category_id": 15, "poly": [220.0, 1620.0, 838.0, 1620.0, 838.0, 1652.0, 220.0, 1652.0], "score": 0.98, "text": ". As intuition would suggest, it is more beneficial to"}, {"category_id": 15, "poly": [127.0, 1584.0, 461.0, 1581.0, 461.0, 1620.0, 127.0, 1623.0], "score": 0.96, "text": " shows the optimal values of"}, {"category_id": 15, "poly": [483.0, 1584.0, 794.0, 1581.0, 794.0, 1620.0, 483.0, 1623.0], "score": 0.99, "text": "for noise ranging between"}, {"category_id": 15, "poly": [134.0, 160.0, 836.0, 160.0, 836.0, 192.0, 134.0, 192.0], "score": 0.99, "text": "over the most recent frames. By maintaining a reasonably"}, {"category_id": 15, "poly": [134.0, 229.0, 836.0, 229.0, 836.0, 261.0, 134.0, 261.0], "score": 0.98, "text": "edges, essentially reducing over-smoothing of a pixel's dis-"}, {"category_id": 15, "poly": [132.0, 261.0, 838.0, 261.0, 838.0, 293.0, 132.0, 293.0], "score": 0.99, "text": "parity when a pixel transitions from one depth to another in"}, {"category_id": 15, "poly": [130.0, 293.0, 354.0, 296.0, 353.0, 328.0, 129.0, 325.0], "score": 1.0, "text": "subsequent frames."}, {"category_id": 15, "poly": [134.0, 192.0, 300.0, 192.0, 300.0, 225.0, 134.0, 225.0], "score": 0.93, "text": "high value of"}, {"category_id": 15, "poly": [330.0, 192.0, 836.0, 192.0, 836.0, 225.0, 330.0, 225.0], "score": 0.99, "text": ", the auxiliary cost also preserves temporal"}, {"category_id": 15, "poly": [132.0, 1345.0, 836.0, 1348.0, 836.0, 1382.0, 132.0, 1380.0], "score": 1.0, "text": "Figure 5: A comparison of stereo matching without temporal"}, {"category_id": 15, "poly": [132.0, 1382.0, 834.0, 1382.0, 834.0, 1414.0, 132.0, 1414.0], "score": 0.98, "text": "cost aggregation (top\uff09 and with temporal cost aggregation"}, {"category_id": 15, "poly": [134.0, 1416.0, 836.0, 1416.0, 836.0, 1446.0, 134.0, 1446.0], "score": 0.98, "text": "(bottom) for a single frame in the synthetic video sequence"}, {"category_id": 15, "poly": [134.0, 1448.0, 337.0, 1446.0, 337.0, 1478.0, 134.0, 1480.0], "score": 0.98, "text": "where the noise is"}, {"category_id": 15, "poly": [392.0, 1448.0, 735.0, 1446.0, 735.0, 1478.0, 392.0, 1480.0], "score": 0.99, "text": "and the feedback coefficient is"}, {"category_id": 15, "poly": [896.0, 855.0, 1324.0, 857.0, 1323.0, 896.0, 896.0, 894.0], "score": 0.95, "text": "1I Millions of Disparity Estimates per Second."}, {"category_id": 15, "poly": [903.0, 912.0, 1550.0, 912.0, 1550.0, 944.0, 903.0, 944.0], "score": 0.99, "text": "3 As measured by the Middlebury stereo performance benchmark using"}, {"category_id": 15, "poly": [901.0, 887.0, 1002.0, 887.0, 1002.0, 919.0, 901.0, 919.0], "score": 0.99, "text": "2Assumes"}, {"category_id": 15, "poly": [1106.0, 887.0, 1404.0, 887.0, 1404.0, 919.0, 1106.0, 919.0], "score": 0.98, "text": "images with 32 disparity levels."}, {"category_id": 15, "poly": [915.0, 937.0, 1036.0, 937.0, 1036.0, 969.0, 915.0, 969.0], "score": 0.96, "text": "the avgerage"}, {"category_id": 15, "poly": [1060.0, 937.0, 1192.0, 937.0, 1192.0, 969.0, 1060.0, 969.0], "score": 0.96, "text": "of bad pixels."}, {"category_id": 15, "poly": [873.0, 1515.0, 1571.0, 1515.0, 1571.0, 1545.0, 873.0, 1545.0], "score": 0.97, "text": "[1] D. Scharstein and R. Szeliski, \u201cA taxonomy and evaluation of dense "}, {"category_id": 15, "poly": [915.0, 1542.0, 1573.0, 1542.0, 1573.0, 1572.0, 915.0, 1572.0], "score": 0.98, "text": "two-frame stereo correspondence algorithms\u201d\u2019 International Journal of"}, {"category_id": 15, "poly": [915.0, 1565.0, 1409.0, 1565.0, 1409.0, 1597.0, 915.0, 1597.0], "score": 0.98, "text": "Computer Vision, vol. 47, pp. 7-42, April-June 2002."}, {"category_id": 15, "poly": [871.0, 1588.0, 1568.0, 1590.0, 1568.0, 1623.0, 871.0, 1620.0], "score": 0.98, "text": "[2]  D. Scharstein and R. 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\ No newline at end of file

demo/magic_pdf_parse_main.py

@@ -69,6 +69,7 @@ def pdf_parse_main(
     :param parse_method: 解析方法， 共 auto、ocr、txt 三种，默认 auto，如果效果不好，可以尝试 ocr
     :param model_json_path: 已经存在的模型数据文件，如果为空则使用内置模型，pdf 和 model_json 务必对应
     :param is_json_md_dump: 是否将解析后的数据写入到 .json 和 .md 文件中，默认 True，会将不同阶段的数据写入到不同的 .json 文件中（共3个.json文件），md内容会保存到 .md 文件中
+    :param is_draw_visualization_bbox: 是否绘制可视化边界框，默认 True，会生成布局框和 span 框的图像
     :param output_dir: 输出结果的目录地址，会生成一个以 pdf 文件名命名的文件夹并保存所有结果
     """
     try:
@@ -97,12 +98,9 @@ def pdf_parse_main(
             model_json = []
 
         # 执行解析步骤
-        # image_writer = DiskReaderWriter(output_image_path)
         image_writer, md_writer = FileBasedDataWriter(output_image_path), FileBasedDataWriter(output_path)
 
         # 选择解析方式
-        # jso_useful_key = {"_pdf_type": "", "model_list": model_json}
-        # pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
         if parse_method == 'auto':
             jso_useful_key = {'_pdf_type': '', 'model_list': model_json}
             pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
@@ -141,5 +139,5 @@ def pdf_parse_main(
 
 # 测试
 if __name__ == '__main__':
-    pdf_path = r'D:\project\20240617magicpdf\Magic-PDF\demo\demo1.pdf'
-    pdf_parse_main(pdf_path)
+    file_path = r'D:\project\20240617magicpdf\Magic-PDF\demo\demo1.pdf'
+    pdf_parse_main(file_path)