This is the official code repository for the work: The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement , presented at CVPR 2022.

If you use parts of this work, or otherwise take inspiration from it, please considering citing our paper:

@inproceedings{chugunov2022implicit,
  title={The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement},
  author={Chugunov, Ilya and Zhang, Yuxuan and Xia, Zhihao and Zhang, Xuaner and Chen, Jiawen and Heide, Felix},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2852--2862},
  year={2022}
}

• Developed using PyTorch 1.10.0 on Linux x64 machine
• Condensed package requirements are in \requirements.txt. Note that this contains the package versions at the time of publishing, if you update to, for example, a newer version of PyTorch you will need to watch out for changes in class/function calls

• Download data from this Google Drive link and unpack into the \data folder
• Each folder corresponds to a scene [castle, double, eagle, elephant, embrace, frog, ganesha, gourd, rocks, thinker] and contains five files.
  • model.pt is the frozen, trained MLP corresponding to the scene
  • frame_bundle.npz is the recorded bundle of data (images, depth, and poses)
  • pose_bundle.npz is a much smaller recorded bundle of data (poses only)
  • reprojected_lidar.npy is the merged LiDAR depth baseline as described in the paper
  • snapshot.mp4 is a video of the recorded snapshot for visualization purposes

An explanation of the format and contents of the frame bundles (frame_bundle.npz) is given in an interactive format in \0_data_format.ipynb. We recommend you go through this jupyter notebook before you record your own bundles or otherwise manipulate the data.

HNDR
  ├── checkpoints  
  │   └── // folder for network checkpoints
  ├── data  
  │   └── // folder for recorded bundle data
  ├── utils  
  │   ├── dataloader.py  // dataloader class for bundle data
  │   ├── neural_blocks.py  // MLP blocks and positional encoding
  │   └── utils.py  // miscellaneous helper functions (e.g. grid/patch sample)
  ├── 0_data_format.ipynb  // interactive tutorial for understanding bundle data
  ├── 1_reconstruction.ipynb  // interactive tutorial for depth reconstruction
  ├── model.py  // the learned implicit depth model
  │             // -> reproject points, query MLP for offsets, visualization
  ├── README.md  // a README in the README, how meta
  ├── requirements.txt  // frozen package requirements
  ├── train.py  // wrapper class for arg parsing and setting up training loop
  └── train.sh  // example script to run training

The jupyter notebook \1_reconstruction.ipynb contains an interactive tutorial for depth reconstruction: loading a model, loading a bundle, generating depth.

The script \train.sh demonstrates a basic call of \train.py to train a model on the gourd scene data. It contains the arguments

• checkpoint_path - path to save model and tensorboard checkpoints
• device - device for training [cpu, cuda]
• bundle_path - path to the bundle data

For other training arguments, see the argument parser section of \train.py.

Best of luck,
Ilya