Surface Reconstruction Benchmark from point clouds: A Survey and a benchmark

This repository contains the official experiment implementation to the the TPAMI-2024 paper Surface Reconstruction Benchmark from point clouds: A Survey and a benchmark.

[Paper] [Dataset] [Project Page]

File Organization

SurfaceRecBenchamrk
    ├──data                     # Put data here
    |   ├──synthetic_object
    |   ├──synthetic_scene
    |   └──real_object 
    ├──build_dataset            # Methods to build our benchmark datasets
    |   ├──scan_and_synthesis
    |   └──preprocessing
    ├──reconstruction           # Reconstruction algorithms
    |
    └──metrics                  # Methods to evaluate the reconstructed surfaces 
        ├──vanilla_metric
        └──neural_metric

How to use

    git clone https://github.com/Huang-ZhangJin/SurfaceRecBenchmark.git
    git submodule update --init --recursive

There is a README.md file in each subfolder that describes how to use each script.

1. Data

Download the Dataset and put it in the data folder

• To synthetic point clouds yourself:
  • To perform object-level synthetic scanning, please follow instructions
  • To perform scene-level synthetic scanning, please follow instructions
• Or use the point clouds provided by us
• To pre-processing the point clouds, please follow instructions

2. Reconstruction Methods

• Some surface reconstruction methods used in our paper
• Optionally, see our 3D Surface Reconstruction Library (Coming Soon!!!)

3. Evaluation Metrics

To use the following evaluation metrics, please follow instructions

• Vanilla metrics
  • Chamfer Distance (CD)
  • F-score
  • Normal Consistency Score (NCS)
• Neural metrics
  • Neural Feature Similarity (NFS)

Citation

If you find our work useful in your research, please consider citing:

@ARTICLE{
    scutsurf_huang,
    author={Huang, ZhangJin and Wen, Yuxin and Wang, ZiHao and Ren, Jinjuan and Jia, Kui},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
    title={Surface Reconstruction from Point Clouds: A Survey and a Benchmark}, 
    year={2024},
    pages={1-20},
    doi={10.1109/TPAMI.2024.3429209}
}