Comments (3)
@Rolandxx7
Thanks for your attention.
1.) MC_MASK_THRES is used in MC loss, you could refer to the formula(4) in the original paper of sec3.3. When the proproblities of belonging to the foreground for a position on two modalities are lower than a specified threshold, we think it is a background point. We do not compute MC loss for these background points. In general, it is proper to set MC_MASK_THRES as 0.2 in a dense scene~(64-beam). For a sparser scene, it is necessary to reduce the value MC_MASK_THRES~(e.g., 0.1 or 0.5).
2.) Waymo provides 360-degree LiDAR points and multiple RGB images. And the point-wise correspondence between two modalities can be obtained by the provided multiple projection matrices.
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@happinesslz
Thanks for your detailed explanation!I have one more question to ask.
"PC_AREA_SCOPE" and "RPN_POST_NMS_TOP_N" defined in PED_EPNet_plus_plus.yaml are different from those in CYC_EPNet_plus_plus.yaml and CAR_EPNet_plus_plus.yaml. Could you please explain the reason for me?
Also, I find that if we use the [[-40, 40], [-0.5, 2.5], [0, 70.4]] as the PC_AREA_SCOPE in PED_EPNet_plus_plus.yaml , the number of objects can be increased, so I would like to know why you did this.
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@Rolandxx7
For Pedestrian, we find setting a small range like offical PointPillars can promise a satisfying performance. Besides, increasing the value of RPN_POST_NMS_TOP_N can improve the recall, which is very important to detection hard Pedestrians.
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Related Issues (17)
- What is the DEEP_RCNN_FUSION? HOT 1
- CB-fusion integration into OpenPCdet HOT 7
- Implement EPNet on Waymo dataset HOT 6
- eval_rcnn TEST mode HOT 5
- Visualization of Fig.8 HOT 1
- Is the train mask necessary for evaluation purposes? HOT 2
- error: command '/usr/bin/g++' failed with exit code 1 HOT 2
- About the training effect of EPNet in OpenPCDet. HOT 1
- The results of the evaluation model are not consistent with the documentation
- RuntimeError: Error compiling objects for extension HOT 5
- When can CB-Fusion be integrated into OpenPCDet?
- CUDA Error while evaluation on KITTI dataset:
- about train_mask when python train_rcnn? HOT 1
- RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`
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