explicit_hybrid_encoding: A Python repository from thua919

Explicit Hybrid Encoding

This repository has been developed largely based on H2-Mapping. We express our sincere gratitude to the authors for their wonderful work! It should be noted that our Explicit Hybrid Encoding is rooted in src/functions/single_grid_net.py and is initiated by the decoder: single_grid_net configuration in configs/scannet.

Plase begin by cloning this repository and all its submodules using the following command:

git clone https://github.com/thua919/explicit_hybrid_encoding

and then, we kindly direct developers to the original H2-Mapping repository for installation instructions. Once done, please follow the data downloading procedure on ScanNet website, and extract color/depth frames from the .sens file using this code.

[Directory structure of ScanNet (click to expand)]

DATAROOT is Datasets by default. If a sequence (sceneXXXX_XX) is stored in other places, please change the input_folder path in the config file or in the command line.

  DATAROOT
  └── scannet
      └── scans
          └── scene0000_00
              └── frames
                  ├── color
                  │   ├── 0.jpg
                  │   ├── 1.jpg
                  │   ├── ...
                  │   └── ...
                  ├── depth
                  │   ├── 0.png
                  │   ├── 1.png
                  │   ├── ...
                  │   └── ...
                  ├── intrinsic
                  └── pose
                      ├── 0.txt
                      ├── 1.txt
                      ├── ...
                      └── ...

Once the data is downloaded and set up properly, please quickly test the running it by:

python -W ignore run_mapping.py configs/ScanNet/scene0000.yaml

Citing

Please consider citing following works when you use this repository:

@ARTICLE{10243098,
  author={Jiang, Chenxing and Zhang, Hanwen and Liu, Peize and Yu, Zehuan and Cheng, Hui and Zhou, Boyu and Shen, Shaojie},
  journal={IEEE Robotics and Automation Letters}, 
  title={H$_{2}$-Mapping: Real-Time Dense Mapping Using Hierarchical Hybrid Representation}, 
  year={2023},
  volume={8},
  number={10},
  pages={6787-6794},
  doi={10.1109/LRA.2023.3313051}}

@inproceedings{nerfslam24hua,
  author = {Tongyan Hua and Lin Wang},
  title = {Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM},
  booktitle = {Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2024},
}