NeurMips: Neural Mixture of Planar Experts for View Synthesis

This is the official repo for PyTorch implementation of paper "NeurMips: Neural Mixture of Planar Experts for View Synthesis", CVPR 2022.

Paper | Project page | Video

🌱 Prerequisites

• OS: Ubuntu 20.04.4 LTS
• GPU: NVIDIA TITAN RTX
• Python package manager conda

🌱 Setup

Datasets

Download and put datasets under folder data/ by running:

bash run/dataset.sh

For more details of file structure and camera convention, please refer to Dataset.

Environment

Install all python packages for training and evaluation with conda environment setup file:

conda env create -f environment.yml
conda activate neurmips

CUDA extension installation

Compile the extension directly by running:

cd cuda/
python setup.py develop

Note that if you need to modify this CUDA code, simply compile again after your modification.

Pretrained models (optional)

Download pretrained model weights for evaluation without training from scratch:

bash run/checkpoints.sh

🌱 Usage

We provide hyperparameters for each experiment in config file configs/*.yaml, which is used for training and evaluation. For example, replica-kitchen.yaml corresponds to Replica dataset Kitchen scene, and tat-barn.yaml corresponds to Tanks&Temple dataset Barn scene.

Training

Train the teacher and experts model by running:

bash run/train.sh [config]
# example: bash run/train.sh replica-kitchen

Evaluation

Render testing images and evaluate metrics (i.e. PSNR, SSIM, LPIPS) by running:

bash run/eval.sh [config]
# example: bash run/eval.sh replica-kitchen

The rendered images are put under folder output_images/[config]/experts/color/valid/

CUDA Acceleration

To render testing images with optimized CUDA code by running:

bash run/eval_fast.sh [config]
# example: bash run/eval_fast.sh replica-kitchen

The rendered images are put under folder output_images/[config]/experts_cuda/color/valid/

BibTex

@inproceedings{lin2022neurmips,
  title={NeurMiPs: Neural Mixture of Planar Experts for View Synthesis},
  author = {Lin, Zhi-Hao and Ma, Wei-Chiu and Hsu, Hao-Yu and Wang, Yu-Chiang Frank and Wang, Shenlong},
  year={2022},
  booktitle={CVPR},
}