# Center-based 3D Object Detection and Tracking > [Center-based 3D Object Detection and Tracking](https://arxiv.org/abs/2006.11275) ## Abstract Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. CenterPoint achieved state-of-the-art performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, CenterPoint outperforms all previous single model method by a large margin and ranks first among all Lidar-only submissions.
## Introduction We implement CenterPoint and provide the result and checkpoints on nuScenes dataset. We follow the below style to name config files. Contributors are advised to follow the same style. `{xxx}` is required field and `[yyy]` is optional. `{model}`: model type like `centerpoint`. `{model setting}`: voxel size and voxel type like `01voxel`, `02pillar`. `{backbone}`: backbone type like `second`. `{neck}`: neck type like `secfpn`. `[dcn]`: Whether to use deformable convolution. `[circle]`: Whether to use circular nms. `[batch_per_gpu x gpu]`: GPUs and samples per GPU, 4x8 is used by default. `{schedule}`: training schedule, options are 1x, 2x, 20e, etc. 1x and 2x means 12 epochs and 24 epochs respectively. 20e is adopted in cascade models, which denotes 20 epochs. For 1x/2x, initial learning rate decays by a factor of 10 at the 8/16th and 11/22th epochs. For 20e, initial learning rate decays by a factor of 10 at the 16th and 19th epochs. `{dataset}`: dataset like nus-3d, kitti-3d, lyft-3d, scannet-3d, sunrgbd-3d. We also indicate the number of classes we are using if there exist multiple settings, e.g., kitti-3d-3class and kitti-3d-car means training on KITTI dataset with 3 classes and single class, respectively. ## Usage ### Test time augmentation We have supported double-flip and scale augmentation during test time. To use test time augmentation, users need to modify the `test_pipeline` and `test_cfg` in the config. For example, we change `centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py` to the following. ```python _base_ = './centerpoint_0075voxel_second_secfpn_circlenms' \ '_4x8_cyclic_20e_nus.py' model = dict( test_cfg=dict( pts=dict( use_rotate_nms=True, max_num=83))) point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0] file_client_args = dict(backend='disk') class_names = [ 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' ] test_pipeline = [ dict( type='LoadPointsFromFile', load_dim=5, use_dim=5, file_client_args=file_client_args), dict( type='LoadPointsFromMultiSweeps', sweeps_num=9, use_dim=[0, 1, 2, 3, 4], file_client_args=file_client_args, pad_empty_sweeps=True, remove_close=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=[0.95, 1.0, 1.05], flip=True, pcd_horizontal_flip=True, pcd_vertical_flip=True, transforms=[ dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[1., 1.], translation_std=[0, 0, 0]), dict(type='RandomFlip3D', sync_2d=False), dict( type='PointsRangeFilter', point_cloud_range=point_cloud_range), dict( type='DefaultFormatBundle3D', class_names=class_names, with_label=False), dict(type='Collect3D', keys=['points']) ]) ] data = dict( val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) ``` ## Results and models ### CenterPoint |Backbone| Voxel type (voxel size) |Dcn|Circular nms| Mem (GB) | Inf time (fps) | mAP |NDS| Download | | :---------: |:-----: |:-----: | :------: | :------------: | :----: |:----: | :------: |:------: | |[SECFPN](./centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.1)|✗|✓|4.9| |56.19|64.43|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205-5db91e00.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201001_135205.log.json)| |above w/o circle nms|voxel (0.1)|✗|✗| | |56.56|64.46|| |[SECFPN](./centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.1)|✓|✓|5.2| |56.34|64.81|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317-26d8176c.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20201004_075317.log.json)| |above w/o circle nms|voxel (0.1)|✓|✗| | |56.60|64.90|| |[SECFPN](./centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.075)|✗|✓|7.8| |57.34|65.23|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905-358fbe3b.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus_20200925_230905.log.json)| |above w/o circle nms|voxel (0.075)|✗|✗| | |57.63|65.39| | |[SECFPN](./centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.075)|✓|✓|8.5| |57.27|65.58|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619-67c8496f.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus_20200930_201619.log.json)| |above w/o circle nms|voxel (0.075)|✓|✗| | |57.43|65.63|| |above w/ double flip|voxel (0.075)|✓|✗| | |59.73|67.39|| |above w/ scale tta|voxel (0.075)|✓|✗| | |60.43|67.65|| |above w/ circle nms w/o scale tta|voxel (0.075)|✓|✗| | |59.52|67.24|| |[SECFPN](./centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|pillar (0.2)|✗|✓|4.4| |49.07|59.66|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716-a134a233.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20201004_170716.log.json)| |above w/o circle nms|pillar (0.2)|✗|✗| | |49.12|59.66|| |[SECFPN](./centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus.py)|pillar (0.2)|✓|✗| 4.6| |48.8 |59.67 |[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722-3bb135f2.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722.log.json)| |above w/ circle nms|pillar (0.2)|✓|✓| | |48.79|59.65|| ## Citation ```latex @article{yin2021center, title={Center-based 3D Object Detection and Tracking}, author={Yin, Tianwei and Zhou, Xingyi and Kr{\"a}henb{\"u}hl, Philipp}, journal={CVPR}, year={2021}, } ```