NISP: Neural Implicit Surface Parameterization

This repository contains the experimental code and data for training a neural network that learns to implicitly represent the parameterization charts of the object surface, as described in the paper "Learning Neural Implicit Representations with Surface Signal Parameterizations".

Data

The experimental data is in the data folder, where data/shapes stores the OBJ files for the experimental 3D objects containing the triangle meshes and UV maps, data/diffuse-maps stores the diffuse maps, data/normal-maps stores the normal maps, and sdf-models stores the pre-trained neural implicit surfaces of the 3D objects learned by OverfitSDF.

Dependencies

All the dependencies are packaged in environment.yml that can be installed using conda.

conda env create -f environment.yml

The conda environment can then be activated.

conda activate nisp

All the following commands are run in this conda environment.

Training

Use main.py for training our model, with the argument --train turned on. For example,

python main.py --train --model_name apple_strudel --fourier_max_freq 10 --use_siren

where --model_name refers to the name of the object, such as apple_strudel, broad_leaf_succulent, and so on (see the files in data/shapes); --fourier_max_freq controls the number of Fourier series the input is encoded into; and --use_siren controls whether the SIREN layer is implemented for the hidden layers. For running other comparative baseline models, please use the argument --texture_model_type, and run with value color or uv.

The trained models and the output files generated during training will be saved in decomposed-uv-mapper (or in color-mapper or uv-mapper) in the results folder, depending on the type of model being trained.

Rendering

Use main.py to render the implicit surface with texture mapping enabled by our model, with --train off. For example,

python main.py --model_name apple_strudel --use_normal_map

where --use_normal_map is optional and controls whether to enable normal mapping or not. Texture mapping results by the comparative baseline models can also be rendered through the argument --texture_model_type. The rendered images will appear in decomposed-uv-mapper (or in color-mapper or uv-mapper) in the results folder.