CoordX

PyTorch implementation of the paper CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture.

Project Page | Paper

Setup

Python dependencies:

• ConfigArgParse
• imageio
• scikit-image
• scipy
• tensorboard
• PyTorch 1.9.1
• Torchvision 0.10.1
• trimesh

Run pip install -r requirements.txt

Running Experiments

Image experiments

Run python experiment_scripts/train_img.py --split_mlp --split_train --experiment_name=image_test to train a CoordX network to fit the default photographer image for 10,000 epochs, results can be found under ./logs/image_test

Some arguments and their definitions:

--model_type
: Options currently are "sine" (all sine activations), "relu" (all relu activations), "nerf" (relu activations and positional encoding as in NeRF), "rbf" (input rbf layer, rest relu), and in the future: "mixed" (first layer sine, other layers tanh)'.

--split_mlp
: Using the split MLP architecture.

--split_train
: Using split training acceleration.

--fusion_operator
: Options currently are "sum" (element-wise summation for features from each channel), "prod" (element-wise multiplication) and "concat" (concatenation).

Video experiments

Run python experiment_scripts/train_video.py --split_mlp --split_train --st_split --experiment_name=video_test to train a CoordX network to fit the default cat_video.mp4 under ./data

--st_split
: Split channels into [pixel location, frame]

3D shape experiments

Run python experiment_scripts/train_3d_occupancy.py --split_mlp --mesh_path=<./path/to/mesh> --experiment_name=shape_test to train a CoordX network to fit a 3d shape stored in ./path/to/mesh

--mesh_path
: Path to the mesh file

Contact

If you have any questions, please feel free to email the authors.