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

Last update: Dec 27, 2022

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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.

Comments

The Hotdog result is terriable？

Hi， your job is brilliant and i'm very interested, i follow your description, run the code on the nerf_synthetic hotdog data, but the result I got was terriable, the psnr in your paper is 25.250, but i just got 8.37. and when i train the model, I found the val images were always splited. Is there any trick that you dont mentioned when train the model? Thanks!

opened by YangHaibo01 1

Consistency Loss - Question on the implementation

Assuming

          args.N_importance = 0
          args.consistency_model_type.startswith('clip_vit') = True

Is it right to say that the consistency loss will compare the first pixel row of the first image of the training batch with the first pixel row of a random image in the target batch ?

            with torch.no_grad():
                  targets_resize_model = F.interpolate(targets, (args.consistency_size, args.consistency_size), ....)
                  target_embeddings = embed(targets_resize_model)  # [N_images , Width , Height]
            target_emb = target_embeddings[:, 0]  # from all images take the first rows
            target_i = np.random.randint(target_emb.shape[0]) 
            target_emb = target_emb[target_i] # sample a random image


            rgbs_resize_c = F.interpolate(rgbs, size=(args.consistency_size, args.consistency_size), mode=args.pixel_interp_mode)
            rendered_embeddings = embed(rgbs_resize_c) # [N_images , Width , Height]
            rendered_embedding = rendered_embeddings[0]  # get the first image
            rendered_emb = rendered_embedding[0] # get the first row of pixels

            consistency_loss = -torch.cosine_similarity(target_emb, rendered_emb, dim=-1)

opened by pbonazzi 0

Strange results on the Lego dataset

Hi, thanks for sharing such great work! I followed the instructions to install dependencies and place datasets. Since I only wanted to see the results for the Lego in nerf-synthesis dataset, I modified the scripts and only make the lego line remain. It runs all good but the results after the 8 views train and ft are strange, just like this.

Could you tell me where is the problem? Thank you very much!

opened by honghd16 0
The meaning of "Diet"NeRF

Hi! Thanks for your excellent work! I am wondering what's the meaning of "putting NeRF on a diet", does it mean less input views or something else?

opened by ghy0324 0
Release checkpoints

Hi, your work is excellent and I'm really interested in it :) Would you mind sharing the pretrained model so that I can compare to it directly?

Thanks.

opened by sj-li 1

Owner

Ajay Jain

AI PhD at Berkeley

GitHub

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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

Comments

The Hotdog result is terriable？

Consistency Loss - Question on the implementation

Strange results on the Lego dataset

The meaning of "Diet"NeRF

Release checkpoints

Owner

Ajay Jain

Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis (CVPR2022)

Instant-nerf-pytorch - NeRF trained SUPER FAST in pytorch

Few-NERD: Not Only a Few-shot NER Dataset

Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

Pytorch implementation of few-shot semantic image synthesis

Official code release for "Learned Spatial Representations for Few-shot Talking-Head Synthesis" ICCV 2021

SCI-AIDE : High-fidelity Few-shot Histopathology Image Synthesis for Rare Cancer Diagnosis

Blender add-on: Add to Cameras menu: View → Camera, View → Add Camera, Camera → View, Previous Camera, Next Camera

(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

Unofficial implementation of One-Shot Free-View Neural Talking Head Synthesis

ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers

This repository contains a PyTorch implementation of "AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis".

Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.

From this paper "SESNet: A Semantically Enhanced Siamese Network for Remote Sensing Change Detection"

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

SeMask: Semantically Masked Transformers for Semantic Segmentation.

Open source repository for the code accompanying the paper 'Non-Rigid Neural Radiance Fields Reconstruction and Novel View Synthesis of a Deforming Scene from Monocular Video'.