Comments (3)
Thanks for your interest! I don't think there is anything wrong in your code.
Actually depth results inferred by regular depth completion models inevitably suffers from artifacts when back projected to 3D space. In other words, not all pixels of the completed depth are reliable. One simple way to select reliable pixels is to predict an additional confidence map and remove pixels with low confidence. You may supervise the confidence prediction network with e^-|D_gt-D|. There also exists some more complex solutions like [DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network. RAL 2021.].
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And one recent work claiming to address the smearing depth problem [Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries. In CVPR 2021]
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And one recent work claiming to address the smearing depth problem [Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries. In CVPR 2021]
Thanks so much for your prompt reply.
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Related Issues (20)
- Runtime measurement HOT 4
- How to use the sparse depth?
- How to use the sparse depth? HOT 4
- Modify the backbone network HOT 3
- lightweight deployment of PENet network
- How to infer PENet for KITTI object task? HOT 3
- broken PNG file HOT 1
- the difference intrinsic parameters between train and test HOT 4
- Modify DA-CSPN++ for single branch input HOT 1
- some questions about the implement of CSPN HOT 4
- Model mismatch at inference time HOT 1
- Tensor Dimension Mismatch HOT 1
- How many parameters dose PENet have? HOT 2
- About training HOT 1
- How to implement the model with ROS?
- can't download the pretrained PENet Model
- Pretrained on NYU dataset?
- RuntimeError: CUDA out of memory.
- confindence map
- lower lidar scanline input
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