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NVlabs / FB-BEV

Official PyTorch implementation of FB-BEV & FB-OCC - Forward-backward view transformation for vision-centric autonomous driving perception

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whether FB-BEV uses BEV backbone?

SPA-junghokim opened this issue · comments

commented

I would like to express my gratitude for your excellent research. I had always been curious about whether using both forward and backward projections for BEV representation would enhance its performance. I had contemplated taking this up as a research topic. I was particularly astounded by the improvement in performance when a module for depth was added during the backward projection.

One aspect I'm curious about is the experiment with ResNet-50 mentioned in your paper. From what I observed, the FLOPS are lower compared to when BEVDepth is reproduced. I anticipate that to achieve this, one cannot use the BEVBackbone + neck portion utilized in BEVDepth. Could you confirm if my assumption is correct?

Thank you.

commented

We have lower FLOPs compared to BEVDepth due to we use smaller feature dimension. We still use BEV-Backbone, which is an important module for final performance.

commented

Thank you for your response! It cleared up a lot of my questions. Once again, I appreciate your research. I hope we can meet at a reputable conference in the future!