Directed Distance Function
This repository contains all the code needed to train a network to learn the directed distance to an object surface. This includes code for data generation, model creation, training, and visualization.
Requirements
- trimesh
- tqdm
- scikit-learn
- matplotlib
- pytorch
- open3d
- pickle (part of standard Python)
- palettable
- rtree
- igl
More requirements for Srinath's v2:
- beacon (
pip install git+https://github.com/brown-ivl/beacon.git) - tk3dv (
pip install git+https://github.com/drsrinathsridhar/tk3dv.git) - [ Required on some machines to avoid an OpenMP issue ]
conda install -c conda-forge nomkl
Data Generation / Sampling Rays
The data generation code offers a few methods for sampling rays with the idea that some sampling techniques produce harder to learn rays (edge cases). To visualize the different sampling methods, use
python sampling.py -v --mesh_file <path to .obj --use_4d>
You can also see how fast each sampling method is by running
python sampling.py -s
Training
To train, test, and save a network, run
python train4D.py -Tts -n mynetwork --mesh_file <path to .obj> --save_dir <dir results can be written in> --intersect_limit <# of intersections>
The flags -d, -v, -p, and -m can be used to create depth images, depth video, point clouds, and meshes respectively. The video will be saved to <save_dir>/depth_videos while the rest of the visualizations will be displayed on screen.
To see all flags use
python train4D.py --help
Codebase Overview
- Training/Testing -
train4D.py - Data Generation -
data.py,sampling.py - Network -
model.py - Utility Functions -
utils.py,rasterization.py - Visualization -
camera.py,visualization.py