CodecNeRF: Toward Fast Encoding and Decoding, Compact, and High-quality Novel-view Synthesis

AAAI 2025

[arXiv] [Project Page]

This repository contains code for "CodecNeRF: Toward Fast Encoding and Decoding, Compact, and High-quality Novel-view Synthesis".

Clone the repository:

git clone https://github.com/Gynjn/CodecNeRF.git

Setting up Environment

Install required packages (CUDA 11.8 example)

conda create -n codecnerf python=3.10 -y
conda activate codecnerf
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install opencv-python imageio imageio-ffmpeg timm configargparse einops dahuffman vector-quantize-pytorch compressai scipy torchmetrics matplotlib loralib pandas gdown transformers jaxtyping tensorboard tensorboardX lpips
pip install -U xformers --index-url https://download.pytorch.org/whl/cu118

Run example

Download the pretrained model weights from https://huggingface.co/anonymous-submit/submission/tree/main place them to checkpoints folder.

Run finetune (ec means W/ entropy coding).

python finetune.py --config configs/finetune.txt
python finetune_ec.py --config configs/finetune_ec.txt

If you want to perform entropy coding on tensor and MLP both, then

python finetune_ec_weight.py --config configs/finetune_ec_weight.txt

We provide a few examples in the configuration folder:

data_path, choices = ['camera', 'cake'];

lrate_mlp, learning rate of MLP LoRA;

lrate_feat, learning rate of feature map;

trank, tensor decomposition rank;

lrank, MLP LoRA rank;

alpha, MLP LoRA alpha;

N_samples, the number of point in uniform sampling;

N_importance, the number of point in importance sampling;

More options refer to the opt.py.

Citing CodecNeRF

@article{kang2024codecnerf,
  title={CodecNeRF: Toward Fast Encoding and Decoding, Compact, and High-quality Novel-view Synthesis},
  author={Kang, Gyeongjin and Lee, Younggeun and Park, Eunbyung},
  journal={arXiv preprint arXiv:2404.04913},
  year={2024}
}