JaxNeuS

This is a JAX implementation of NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction.

Installation

recommend using conda

conda env create --file conda.yaml
conda activate jaxneus

prepare datasets

./download.sh

Running

• Training without masks

python exp_runner.py --mode train --conf ./confs/womask.conf --case <case_name>

• Training with masks

python exp_runner.py --mode train --conf ./confs/wmask.conf --case <case_name>

• Extract surface from trained model

python exp_runner.py --mode validate_mesh --conf <config_file> --case <case_name> --is_continue # use latest checkpoint

The corresponding mesh can be found in exp/<case_name>/<exp_name>/meshes/<iter_steps>.ply.

• View interpolation

python exp_runner.py --mode interpolate_<img_idx_0>_<img_idx_1> --conf <config_file> --case <case_name> --is_continue # use latest checkpoint

The corresponding image set of view interpolation can be found in exp/<case_name>/<exp_name>/render/.

Notes

Follow the NeuS code structure and command line for experiments. The rendering function is designed to handle a single ray and vectorized to apply to a batch of rays. The rendering procedure is optimized for speed using just-in-time compilation (JIT).