This is a lightweight python script that fuses multiple registered color and depth images into a projective truncated signed distance function (TSDF) volume, which can then be used to create high quality 3D surface meshes and point clouds. Tested on Ubuntu 16.04.
An older CUDA/C++ version can be found here.
pip install --user numpy opencv-python scikit-image numbapip install --user pycudaThis demo fuses 1000 RGB-D images from the 7-scenes dataset into a 405 x 264 x 289 projective TSDF voxel volume with 2cm resolution at about 30 FPS in GPU mode (0.4 FPS in CPU mode), and outputs a 3D mesh mesh.ply which can be visualized with a 3D viewer like Meshlab.
Note: color images are saved as 24-bit PNG RGB, depth images are saved as 16-bit PNG in millimeters.
python demo.pyThis repository is a part of 3DMatch Toolbox. If you find this code useful in your work, please consider citing:
@inproceedings{zeng20163dmatch,
title={3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions},
author={Zeng, Andy and Song, Shuran and Nie{\ss}ner, Matthias and Fisher, Matthew and Xiao, Jianxiong and Funkhouser, Thomas},
booktitle={CVPR},
year={2017}
}