Neural Point-Based Graphics

This repository provides evaluation dataset used in Neural Point-Based Graphics. We provide training data, test trajectories and our results.

Dataset

ScanNet

Users must agree to the terms of use to download any part of the ScanNet dataset. We provide a script to build point clouds from the raw data.

• Go to https://github.com/ScanNet/ScanNet to obtain ScanNet dataset.
• Download scene0000_00 and scene0024_00
• Export color, depth, poses and intrinsics from *.sens files with this code https://github.com/ScanNet/ScanNet/tree/master/SensReader/python
• Build point clouds:

Install Python requirements to build point clouds:

virtualenv --python=python3.6 venv
source venv/bin/activate
pip install -r requirements.txt

Run scripts on scene folders where you extracted the data:

python build_pointcloud.py --input /path/to/scene0000_00
python build_pointcloud.py --input /path/to/scene0024_00

Script requires the following folder structure:

scene
- pose
- color
- depth
- intrinsic

It will output paths to created *.ply files.

Photogrammetry

Download Plant and Shoe scenes from here. These scenes where built using Samsung S10 camera and Agisoft Metashape.

Folder structure:

scene
- color - N RGB frames
- pointcloud.ply - point cloud
- mesh.ply - mesh
- proj_matrix.txt - OpenGL projection matrix
- view_matrix.txt - N stacked 4x4 train view matrices

Results

Download results from here.

Folder structure:

scene
- test_traj.txt - 4x4 test view matrices
- test_traj.mp4 - our results*

* We use "Ours-full" method from the paper

Citation

@misc{aliev2019neural,
    title={Neural Point-Based Graphics},
    author={Kara-Ali Aliev and Dmitry Ulyanov and Victor Lempitsky},
    year={2019},
    eprint={1906.08240},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

License

The data is released under the CC BY-SA 3.0 license, and the code is released under the MIT license.