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Depth Filler Net (DFNet)

Model

The architecture of our proposed end-to-end depth completion network DFNet is shown as follows. Our network utilizes a U-Net architecture with CDCD blocks, CDC blocks and CDCU blocks. These blocks are mainly composed of dense blocks, with DUC replacing deconvolution layer in up-sampling of CDCU block. All convolution layers except the last one are followed by batch normalizations and ReLU activations, and have 3 × 3 kernels.

Network

Note. The final ReLU activations is used to guarantee that the refined depth is non-negative.

Experiments

Method RMSE REL MAE Delta 1.05 Delta 1.10 Delta 1.25 GPU Mem. Occ. Infer. Time Model Size
ClearGrasp 0.054 0.083 0.037 50.48 68.68 95.28 2.1 GB 2.2813s 934 MB
LIDF-Refine 0.019 0.034 0.015 78.22 94.26 99.80 6.2 GB 0.0182s 251 MB
TranspareNet* 0.026 0.023 0.013 88.45 96.25 99.42 1.9 GB 0.0354s 336 MB
DFNet** 0.018 0.026 0.013 84.94 96.57 99.85 1.6 GB 0.0166s 5.2 MB

Here, ClearGrasp refers to [1], LIDF-Refine refers to [2], TranspareNet refers to [3], and DFNet refers to our proposed Depth Filler Net.

*: TranspareNet [3] is a concurrent work with our project.

**: Here, we use the newly released checkpoint of DFNet, which is slightly different from the checkpoint used in the paper. The newly released checkpoint fixes the bugs of point cloud shifting mentioned in Issue #4 and the black-hole problem mentioned in Issue #7.

For original checkpoint that is used in the paper, please use this version of the repository, and see Google Drive or Baidu Netdisk (Code: c01g) for downloading it. Many thanks to @cxt98 for fixing the bugs in Issue #5.

References

  1. Sajjan, Shreeyak, et al. "Clear grasp: 3d shape estimation of transparent objects for manipulation." 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020.
  2. Zhu, Luyang, et al. "RGB-D Local Implicit Function for Depth Completion of Transparent Objects." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
  3. Xu, Haoping, et al. "Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects." 5th Annual Conference on Robot Learning. 2021.