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

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

Use your Philips Hue lights as Racing Flags. Works with Assetto Corsa, Assetto Corsa Competizione and iRacing.

An official implementation of the paper Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

A transformer model to predict pathogenic mutations

PyTorch implementation of Higher Order Recurrent Space-Time Transformer

[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Survival analysis in Python

Facial Image Inpainting with Semantic Control

DNA-RECON { Automatic Web Reconnaissance Tool }

Trading Strategies for Freqtrade

This folder contains the implementation of the multi-relational attribute propagation algorithm.

A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maximum bidding

Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

PyTorch implementation of SwAV (Swapping Assignments between Views)

Official Pytorch implementation of "DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network" (CVPR'21)

A simple implementation of Kalman filter in Multi Object Tracking

OpenMMLab Detection Toolbox and Benchmark