Awesome
Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot Projection
This is the official repo for the implementation of the dataset generateion part of the CVPR2022 paper: Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot Projection.
Introduction
<div align="center"> <img src=static/dataset.png width=60% /> </div>Since it is difficult to simultaneously acquire accurate depth and spectral reflectance as a large-scale ground-truth dataset in real-world situations, we developed a spectral renderer to generate a synthetic dataset with rendered RGB color-dot images, ground-truth disparity maps, and ground-truth spectral reflectance images by extending the algorithm of a structured-light renderer.
Usage
Dependencies
The python packages can be installed with anaconda:
conda install --file requirements.txt
Building
First make sure the correct CUDA_LIBRARY_PATH is set in config.json.
Afterwards, the renderer can be build by running make within the renderer directory.
Running
First, download ShapeNet V2 and change SHAPENET_ROOT in config.json.
Then the data can be generated and saved to DATA_ROOT in config.json by running
python create_syn_data.py
Example
The generated dataset example including 8448 scenes for training (458.6GB) and 256 scenes for testing (13.9GB) can be downloaded here.
Acknowledgement
The code structure and some code snippets (rasterisation, shading, etc.) are borrowed from Connecting the Dots. Thanks for this great project.