We provide scipts for multi-modality/single-modality and indoor/outdoor 3D detection demos. The pre-trained models can be downloaded from [model zoo](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/model_zoo.md). We provide pre-processed sample data from KITTI and SUN RGB-D dataset. You can use any other data following our pre-processing steps.
We provide scipts for multi-modality/single-modality and indoor/outdoor 3D detection demos. The pre-trained models can be downloaded from [model zoo](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/model_zoo.md/). We provide pre-processed sample data from KITTI and SUN RGB-D dataset. You can use any other data following our pre-processing steps.
## Testing
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@@ -28,7 +28,7 @@ Example on SUN RGB-D data using [VoteNet](https://github.com/open-mmlab/mmdetect
Remember to convert the VoteNet checkpoint if you are using mmdetection3d version >= 0.6.0. See its [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/README.md) for detailed instructions on how to convert the checkpoint.
Remember to convert the VoteNet checkpoint if you are using mmdetection3d version >= 0.6.0. See its [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/README.md/) for detailed instructions on how to convert the checkpoint.
**Notice**: For evaluation on waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`.(Sometimes when using bazel to build `compute_detection_metrics_main`, an error `'round' is not a member of 'std'` may appear. We just need to remove the `std::` before `round` in that file.) `pklfile_prefix` should be given in the options for the bin file generation. For metrics, `waymo` is the recommended official evaluation prototype. Currently, evaluating with choice `kitti` is adapted from KITTI and the results for each difficulty are not exactly the same as the definition of KITTI. Instead, most of objects are marked with difficulty 0 currently, which will be fixed in the future. The reasons of its instability include the large computation for evalution, the lack of occlusion and truncation in the converted data, different definition of difficulty and different methods of computing average precision.
**Notice**: For evaluation on waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`.(Sometimes when using bazel to build `compute_detection_metrics_main`, an error `'round' is not a member of 'std'` may appear. We just need to remove the `std::` before `round` in that file.) `pklfile_prefix` should be given in the options for the bin file generation. For metrics, `waymo` is the recommended official evaluation prototype. Currently, evaluating with choice `kitti` is adapted from KITTI and the results for each difficulty are not exactly the same as the definition of KITTI. Instead, most of objects are marked with difficulty 0 currently, which will be fixed in the future. The reasons of its instability include the large computation for evalution, the lack of occlusion and truncation in the converted data, different definition of difficulty and different methods of computing average precision.
8. Test PointPillars on waymo with 8 GPUs, generate the bin files and make a submission to the leaderboard.
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**Notice**: After generating the bin file, you can simply build the binary file `create_submission` and use them to create a submission file by following the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md). For evaluation on the validation set with the eval server, you can also use the same way to generate a submission.
**Notice**: After generating the bin file, you can simply build the binary file `create_submission` and use them to create a submission file by following the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/). For evaluation on the validation set with the eval server, you can also use the same way to generate a submission.
- Move the key `train_cfg` and `test_cfg` into the model configs (#307)
- Update [README](https://github.com/open-mmlab/mmdetection3d/blob/master/README.md) with [Chinese version](https://github.com/open-mmlab/mmdetection3d/blob/master/README_zh-CN.md) and [instructions for getting started](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/getting_started.md). (#310, #316)
- Update [README](https://github.com/open-mmlab/mmdetection3d/blob/master/README.md/) with [Chinese version](https://github.com/open-mmlab/mmdetection3d/blob/master/README_zh-CN.md/) and [instructions for getting started](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/getting_started.md/). (#310, #316)
- Support a faster and more memory-efficient implementation of DynamicScatter (#318, #326)
#### Bug Fixes
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@@ -127,7 +127,7 @@
- Add tag to README in configurations for specifying different uses (#262)
- Update instructions for evaluation metrics in the documentation (#265)
- Add nuImages entry in [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/README.md) and gif demo (#266, #268)
- Add nuImages entry in [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/README.md/) and gif demo (#266, #268)
- Add unit test for voxelization (#275)
#### Bug Fixes
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- Fixed the issue of unpacking size in [furthest_point_sample.py](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/ops/furthest_point_sample/furthest_point_sample.py)(#248)
- Fix bugs for 3DSSD triggered by empty ground truths (#258)
- Remove models without checkpoints in model zoo statistics of documentation (#259)
- Fix some unclear installation instructions in [getting_started.md](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/getting_started.md)(#269)
- Fix some unclear installation instructions in [getting_started.md](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/getting_started.md/)(#269)
- Fix relative paths/links in the documentation (#271)
- Fix a minor bug in [scatter_points_cuda.cu](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/ops/voxel/src/scatter_points_cuda.cu) when num_features != 4 (#275)
- Fix the bug about missing text files when testing on KITTI (#278)
@@ -16,7 +16,7 @@ We adopt new pre-processing and conversion steps of ScanNet dataset. In previous
- There is an `IndoorPointSample` pipeline in the data pipelines for ScanNet detection task which down-samples points. So removing down-sampling in data generation will not affect the code.
We have trained a [VoteNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/votenet_8x8_scannet-3d-18class.py) model on the newly processed ScanNet dataset and get similar benchmark results. In order to prepare ScanNet data for both detection and segmentation tasks, please re-run the new pre-processing scripts following the ScanNet [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/README.md).
We have trained a [VoteNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/votenet_8x8_scannet-3d-18class.py) model on the newly processed ScanNet dataset and get similar benchmark results. In order to prepare ScanNet data for both detection and segmentation tasks, please re-run the new pre-processing scripts following the ScanNet [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/README.md/).
## MMDetection3D 0.12.0
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- Convert calibration matrices from double to float to avoid type mismatch in further operations.
- Add instructions in the documents on preparing image data.
Please refer to the SUNRGBD [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/data/sunrgbd/README.md) for more details.
Please refer to the SUNRGBD [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/data/sunrgbd/README.md/) for more details.
## MMDetection3D 0.6.0
### VoteNet model structure update
In MMDetection 0.6.0, we updated the model structure of VoteNet, therefore model checkpoints generated by MMDetection < 0.6.0 should be first converted to a format compatible with the latest VoteNet structure via this [script](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_votenet_checkpoints.py). For more details, please refer to the VoteNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet/README.md)
In MMDetection 0.6.0, we updated the model structure of VoteNet, therefore model checkpoints generated by MMDetection < 0.6.0 should be first converted to a format compatible with the latest VoteNet structure via this [script](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_votenet_checkpoints.py). For more details, please refer to the VoteNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet/README.md/)
We list some potential troubles encountered by users and developers, along with their corresponding solutions. Feel free to enrich the list if you find any frequent issues and contribute your solutions to solve them. If you have any trouble with environment configuration, model training, etc, please create an issue using the [provided templates](https://github.com/open-mmlab/mmdetection3d/blob/master/.github/ISSUE_TEMPLATE/error-report.md) and fill in all required information in the template.
We list some potential troubles encountered by users and developers, along with their corresponding solutions. Feel free to enrich the list if you find any frequent issues and contribute your solutions to solve them. If you have any trouble with environment configuration, model training, etc, please create an issue using the [provided templates](https://github.com/open-mmlab/mmdetection3d/blob/master/.github/ISSUE_TEMPLATE/error-report.md/) and fill in all required information in the template.
- `--nproc`: number of workers for data preparation, defaults to `4`. Larger number could reduce the preparation time as images are processed in parallel.
- `--extra-tag`: extra tag of the annotations, defaults to `nuimages`. This can be used to separate different annotations processed in different time for study.
More details could be referred to the [doc](https://mmdetection3d.readthedocs.io/en/latest/data_preparation.html) for dataset preparation and [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.md) for nuImages dataset.
More details could be referred to the [doc](https://mmdetection3d.readthedocs.io/en/latest/data_preparation.html) for dataset preparation and [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.md/) for nuImages dataset.
