Thank you very much for your code.
I would like to ask a question regarding BEV semantic segmentation. The point cloud used for feature extraction covers the front, back, left, and right directions, but the segmentation labels only take the front half (x in 0 to 32m).
During training, the extracted features are not truncated before computing the loss. I'm wondering if this would cause the feature extraction to focus only on the front part of the information? Is this intentionally designed ?
Thank you very much for your code.
I would like to ask a question regarding BEV semantic segmentation. The point cloud used for feature extraction covers the front, back, left, and right directions, but the segmentation labels only take the front half (x in 0 to 32m).
During training, the extracted features are not truncated before computing the loss. I'm wondering if this would cause the feature extraction to focus only on the front part of the information? Is this intentionally designed ?