Comments (1)
Great point!
We did not consider this when CVPR 2024 submission, due to at that time, we mainly considered the downstream performance.
This is actually a big issue in the current codebase for rendering point cloud, due to the miss aligned ego pose and lidar sensor pose. However, to align with our CVPR version, we decide not to modify this when publishing the code.
During training or testing on OpenScene, you can manually adjust the origin of rendering to something like [0, 0, 1.6m] instead of [0, 0, 0] (as the original code) to avoid this issue. But I am not sure how to deal with it for private_test, maybe some alignment of the origin (since in private_test, the rays also start from the origin)?
Thanks,
Zetong
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Related Issues (20)
- About the mini train.pkl and val.pkl HOT 1
- When will the private test set be released? HOT 1
- MMCV, RuntimeError: modulated_deformable_im2col_impl: implementation for device cuda:0 not found. HOT 2
- The performance of ViDAR in private_test_wm dataset HOT 2
- A simple question of sensor coordinate. HOT 2
- ModuleNotFoundError: No module named 'DCNv3' HOT 1
- File "/home/uids0529/.local/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1418, in _write_ninja_file_and_build_library verify_ninja_availability() File "/home/uids0529/.local/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1474, in verify_ninja_availability raise RuntimeError("Ninja is required to load C++ extensions") RuntimeError: Ninja is required to load C++ extensions
- RuntimeError: CUDA error: no kernel image is available for execution on the device 2024-05-15T10:49:54.910116296Z CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. 2024-05-15T10:49:54.910125144Z For debugging consider passing CUDA_LAUNCH_BLOCKING=1 HOT 2
- can a single 3090 be used to pretrain ViDAR? HOT 2
- About point cloud forecasting in paper table2 HOT 3
- About merge_nusc_fullset_pkl.py HOT 2
- About time cost HOT 1
- How to reproduce Figure3 in the paper? HOT 4
- About reference points in temporal cross-attention HOT 3
- 不存在这个包lyft_converter HOT 1
- 训练时缺少config文件
- About custom datasets and num_pred_height in config HOT 2
- 修改哪些参数可以保证用较少的GPU达到训练模型的地步 HOT 1
- nuscenes data entry
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