Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

Last update: Dec 27, 2022

Related tags

Overview

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

This repository contains the official implementation of DietNeRF, a system that reconstructs 3D scenes from a few posed photos.

Setup

We use the following folder structure:

dietnerf/
  logs/ (images, videos, checkpoints)
  data/
    nerf_synthetic/
  configs/ (run configuration files)
CLIP/ (Fork of OpenAI's clip repository with a wrapper)

Create conda environment:

conda create -n dietnerf python=3.9
conda activate dietnerf

Set up requirements and our fork of CLIP:

pip install -r requirements.txt
cd CLIP
pip install -e .

wandb login

Experiments on the Realistic Synthetic dataset

Realistic Synthetic experiments are implemented in the ./dietnerf subdirectory.

You need to download datasets from NeRF's Google Drive folder. The dataset was used in the original NeRF paper by Mildenhall et al. For example,

mkdir dietnerf/logs/ dietnerf/data/
cd dietnerf/data
pip install gdown
gdown --id 18JxhpWD-4ZmuFKLzKlAw-w5PpzZxXOcG -O nerf_synthetic.zip
unzip nerf_synthetic.zip
rm -r __MACOSX

Then, shrink images to 400x400:

python dietnerf/scripts/bulk_shrink_images.py "dietnerf/data/nerf_synthetic/*/*/*.png" dietnerf/data/nerf_synthetic_400_rgb/ True

These images are used for FID/KID computation. The dietnerf/run_nerf.py training and evaluation code automatically shrinks images with the --half_res argument.

Each experiment has a config file stored in dietnerf/configs/. Scripts in dietnerf/scripts/ can be run to train and evaluate models. Run these scripts from ./dietnerf. The scripts assume you are running one script at a time on a server with 8 NVIDIA GPUs.

cd dietnerf
export WANDB_ENTITY=
   
    

# NeRF baselines
sh scripts/run_synthetic_nerf_100v.sh
sh scripts/run_synthetic_nerf_8v.sh
sh scripts/run_synthetic_simplified_nerf_8v.sh

# DietNeRF with 8 observed views
sh scripts/run_synthetic_dietnerf_8v.sh
sh scripts/run_synthetic_dietnerf_ft_8v.sh

# NeRF and DietNeRF with partial observability
sh scripts/run_synthetic_unseen_side_14v.sh

Experiments on the DTU dataset

Coming soon. Our paper also fine-tunes pixelNeRF on DTU scenes for 1-shot view synthesis.

Citation and acknowledgements

If DietNeRF is relevant to your project, please cite our associated paper:

@InProceedings{Jain_2021_ICCV,
    author    = {Jain, Ajay and Tancik, Matthew and Abbeel, Pieter},
    title     = {Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5885-5894}
}

This code is based on Yen-Chen Lin's PyTorch implementation of NeRF and the official pixelNeRF code.

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

Related tags

Overview

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

Setup

Experiments on the Realistic Synthetic dataset

Experiments on the DTU dataset

Citation and acknowledgements

Owner

Ajay Jain

Harmonic Memory Networks for Graph Completion

Koopman operator identification library in Python

Winning Solution in NTIRE19 Challenges on Video Restoration and Enhancement (CVPR19 Workshops) - Video Restoration with Enhanced Deformable Convolutional Networks. EDVR has been merged into BasicSR and this repo is a mirror of BasicSR.

an Evolutionary Algorithm assisted GAN

Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

Emotional conditioned music generation using transformer-based model.

FishNet: One Stage to Detect, Segmentation and Pose Estimation

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

A repository for storing njxzc final exam review material

The author's officially unofficial PyTorch BigGAN implementation.

Realtime YOLO Monster Detection With Non Maximum Supression

PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

DeiT: Data-efficient Image Transformers

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it

Github project for Attention-guided Temporal Coherent Video Object Matting.

Trainable PyTorch reproduction of AlphaFold 2

Pytorch implementation of CoCon: A Self-Supervised Approach for Controlled Text Generation

Machine learning framework for both deep learning and traditional algorithms

A PyTorch implementation of the continual learning experiments with deep neural networks