For the 3D occupancy prediction task training, do we still need to set ignore_index=0 when initiate the cross_entroy loss function? In the paper, you said "pseudo-per-voxel labels were generated from sparse point cloud by assigning a new label of empty to any voxel that does not contain any point, and we use voxel predictions as input to both lovasz-softmax and cross-entropy losses." Does that mean for the [100,100,8] 3D volume, we set all rest voxel 's label to 0 as empty label? I found the occupied voxels whose label generate from sparse lidar point, only has around 1000~2300 in total. This approach will cause serious class imbalance. Can author provide more detail information about empty voxel label generation for the 3D occupancy prediction task?
Hi Author,
For the 3D occupancy prediction task training, do we still need to set ignore_index=0 when initiate the cross_entroy loss function? In the paper, you said "pseudo-per-voxel labels were generated from sparse point cloud by assigning a new label of empty to any voxel that does not contain any point, and we use voxel predictions as input to both lovasz-softmax and cross-entropy losses." Does that mean for the [100,100,8] 3D volume, we set all rest voxel 's label to 0 as empty label? I found the occupied voxels whose label generate from sparse lidar point, only has around 1000~2300 in total. This approach will cause serious class imbalance. Can author provide more detail information about empty voxel label generation for the 3D occupancy prediction task?