Question about the parameters for train and eval

[Rolandxx7]

Question about the parameters for train and eval

[happinesslz]

@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.

[Rolandxx7]

@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.

[happinesslz]

@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.