ibadami / voxlets

Code for the upcoming CVPR 2016 paper

Code for upcoming CVPR 2016 paper

@inproceedings{firman-cvpr-2016,
  author = {Michael Firman and Diego Thomas and Simon Julier and Akihiro Sugimoto},
  title = {{Structured Completion of Unobserved Voxels from a Single Depth Image}},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  year = {2016}
}

Downloading the dataset

The dataset can be downloaded from:

https://dl.dropboxusercontent.com/u/495646/voxlets_dataset.zip

This is a 395MB zip file. You will have to change some of the paths in the code to the location you have extracted the dataset to.

Getting started with the dataset

An example iPython notebook file loading a ground truth TSDF grid and plotting on the same axes as a depth image is given in src/examples/Voxel_data_io_example.ipynb

Code overview

The code is roughly divided into three areas:

1. src/common/ is a Python module containing all the classes and functions used for manipulation of the data and running of the routines. Files included are:

   • images.py - classes for RGBD images and videos
   • camera.py - a camera class, enabling points in 3D to be projected into a virtual depth image and vice versa
   • mesh.py - a class for 3D mesh data, including IO and marching cubes conversion
   • voxel_data.py - classes for 3D voxel grids, including various manipulation routines and ability to copy data between grids at different locations in world space

2. src/pipeline/ - Contains scripts for loading data, performing processing and saving files out. The pipeline as described in the CVPR paper.

3. src/examples/ - iPython notebooks containing examples of use of the data and code.

Prerequisites

I have run this code using a fairly up-to-date version of Anaconda on Ubuntu 14.04.

This probably includes everything you need, but soon I will check to see if there are any requirements which are not included in Anaconda.