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

Overview

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

Official implementation of ViewFormer. ViewFormer is a NeRF-free neural rendering model based on the transformer architecture. The model is capable of both novel view synthesis and camera pose estimation. It is evaluated on previously unseen 3D scenes.

Paper    Web    Demo


Open In Colab Python Versions

Getting started

Start by creating a python 3.8 venv. From the activated environment, you can run the following command in the directory containing setup.py:

pip install -e .

Getting datasets

In this section, we describe how you can prepare the data for training. We assume that you have your environment ready and you want to store the dataset into {output path} directory.

Shepard-Metzler-Parts-7

Please, first visit https://github.com/deepmind/gqn-datasets.

viewformer-cli dataset generate \
    --loader sm7 \
    --image-size 128 \
    --output {output path}/sm7 \
    --max-sequences-per-shard 2000 \
    --split train

viewformer-cli dataset generate \
    --loader sm7 \
    --image-size 128 \
    --output {output path}/sm7 \
    --max-sequences-per-shard 2000 \
    --split test

InteriorNet

Download the dataset into the directory {source} by following the instruction here: https://interiornet.org/. Then, proceed as follows:

viewformer-cli dataset generate \
    --loader interiornet \
    --path {source} \
    --image-size 128  \
    --output {output path}/interiornet \
    --max-sequences-per-shard 50 \
    --shuffle \
    --split train

viewformer-cli dataset generate \
    --loader interiornet \
    --path {source} \
    --image-size 128  \
    --output {output path}/interiornet \
    --max-sequences-per-shard 50 \
    --shuffle \
    --split test

Common Objects in 3D

Download the dataset into the directory {source} by following the instruction here: https://ai.facebook.com/datasets/CO3D-dataset.

Install the following dependencies: plyfile>=0.7.4 pytorch3d. Then, generate the dataset for 10 categories as follows:

viewformer-cli dataset generate \
    --loader co3d \
    --path {source} \
    --image-size 128  \
    --output {output path}/co3d \
    --max-images-per-shard 6000 \
    --shuffle \
    --categories "plant,teddybear,suitcase,bench,ball,cake,vase,hydrant,apple,donut" \
    --split train

viewformer-cli dataset generate \
    --loader co3d \
    --path {source} \
    --image-size 128  \
    --output {output path}/co3d \
    --max-images-per-shard 6000 \
    --shuffle \
    --categories "plant,teddybear,suitcase,bench,ball,cake,vase,hydrant,apple,donut" \
    --split val

Alternatively, generate the full dataset as follows:

viewformer-cli dataset generate \
    --loader co3d \
    --path {source} \
    --image-size 128  \
    --output {output path}/co3d \
    --max-images-per-shard 6000 \
    --shuffle \
    --split train

viewformer-cli dataset generate \
    --loader co3d \
    --path {source} \
    --image-size 128  \
    --output {output path}/co3d \
    --max-images-per-shard 6000 \
    --shuffle \
    --split val

ShapeNet cars and chairs dataset

Download and extract the SRN datasets into the directory {source}. The files can be found here: https://drive.google.com/drive/folders/1OkYgeRcIcLOFu1ft5mRODWNQaPJ0ps90.

Then, generate the dataset as follows:

viewformer-cli dataset generate \
    --loader shapenet \
    --path {source} \
    --image-size 128  \
    --output {output path}/shapenet-{category}/shapenet \
    --categories {category} \
    --max-sequences-per-shard 50 \
    --shuffle \
    --split train

viewformer-cli dataset generate \
    --loader shapenet \
    --path {source} \
    --image-size 128  \
    --output {output path}/shapenet-{category}/shapenet \
    --categories {category} \
    --max-sequences-per-shard 50 \
    --shuffle \
    --split test

where {category} is either cars or chairs.

Faster preprocessing

In order to make the preprocessing faster, you can add --shards {process id}/{num processes} to the command and run multiple instances of the command in multiple processes.

Training the codebook model

The codebook model training uses the PyTorch framework, but the resulting model can be loaded by both TensorFlow and PyTorch. The training code was also prepared for TensorFlow framework, but in order to get the same results as published in the paper, PyTorch code should be used. To train the codebook model on 8 GPUs, run the following code:

viewformer-cli train codebook \
    --job-dir . \
    --dataset "{dataset path}" \
    --num-gpus 8 \
    --batch-size 352 \
    --n-embed 1024 \
    --learning-rate 1.584e-3 \
    --total-steps 200000

Replace {dataset path} by the real dataset path. Note that you can use more than one dataset. In that case, the dataset paths should be separated by a comma. Also, if the size of dataset is not large enough to support sharding, you can reduce the number of data loading workers by using --num-val-workers and --num-workers arguments. The argument --job-dir specifies the path where the resulting model and logs will be stored. You can also use the --wandb flag, that enables logging to wandb.

Finetuning the codebook model

If you want to finetune an existing codebook model, add --resume-from-checkpoint "{checkpoint path}" to the command and increase the number of total steps.

Transforming the dataset into the code representation

Before the transformer model can be trained, the dataset has to be transformed into the code representation. This can be achieved by running the following command (on a single GPU):

viewformer-cli generate-codes \
    --model "{codebook model checkpoint}" \
    --dataset "{dataset path}" \
    --output "{code dataset path}" \
    --batch-size 64 

We assume that the codebook model checkpoint path (ending with .ckpt) is {codebook model checkpoint} and the original dataset is stored in {dataset path}. The resulting dataset will be stored in {code dataset path}.

Training the transformer model

To train the models with the same hyper-parameters as in the paper, run the commands from the following sections based on the target dataset. We assume that the codebook model checkpoint path (ending with .ckpt) is {codebook model checkpoint} and the associated code dataset is located in {code dataset path}. All commands use 8 GPUs (in our case 8 NVIDIA A100 GPUs).

InteriorNet training

viewformer-cli train transformer \
    --dataset "{code dataset path}" \
    --codebook-model "{codebook model checkpoint}" \
    --sequence-size 20 \
    --n-loss-skip 4 \
    --batch-size 40 \
    --fp16 \
    --total-steps 200000 \
    --localization-weight 5. \
    --learning-rate 8e-5 \
    --weight-decay 0.01 \
    --job-dir . \
    --pose-multiplier 1.

For the variant without localization, use --localization-weight 0. Similarly, for the variant without novel view synthesis, use --image-generation-weight 0.

CO3D finetuning

In order to finetune the model for 10 categories, use the following command:

viewformer-cli train finetune-transformer \
    --dataset "{code dataset path}" \
    --codebook-model "{codebook model checkpoint}" \
    --sequence-size 10 \
    --n-loss-skip 1 \
    --batch-size 80 \
    --fp16 \
    --localization-weight 5 \
    --learning-rate 1e-4 \
    --total-steps 40000 \
    --epochs 40 \
    --weight-decay 0.05 \
    --job-dir . \
    --pose-multiplier 0.05 \
    --checkpoint "{interiornet transformer model checkpoint}"

Here {interiornet transformer model checkpoint} is the path to the InteriorNet checkpoint (usually ending with weights.model.099-last). For the variant without localization, use --localization-weight 0.

For all categories and including localization:

viewformer-cli train finetune-transformer \
    --dataset "{code dataset path}" \
    --codebook-model "{codebook model checkpoint}" \
    --sequence-size 10 \
    --n-loss-skip 1 \
    --batch-size 40 \
    --localization-weight 5 \
    --gradient-clip-val 1. \
    --learning-rate 1e-4 \
    --total-steps 100000 \
    --epochs 100 \
    --weight-decay 0.05 \
    --job-dir . \
    --pose-multiplier 0.05 \
    --checkpoint "{interiornet transformer model checkpoint}"

Here {interiornet transformer model checkpoint} is the path to the InteriorNet checkpoint (usually ending with weights.model.099-last).

For all categories without localization:

viewformer-cli train finetune-transformer \
    --dataset "{code dataset path}" \
    --codebook-model "{codebook model checkpoint}" \
    --sequence-size 10 \
    --n-loss-skip 1 \
    --batch-size 40 \
    --localization-weight 5 \
    --learning-rate 1e-4 \
    --total-steps 100000 \
    --epochs 100 \
    --weight-decay 0.05 \
    --job-dir . \
    --pose-multiplier 0.05 \
    --checkpoint "{interiornet transformer model checkpoint}"

Here {interiornet transformer model checkpoint} is the path to the InteriorNet checkpoint (usually ending with weights.model.099-last).

7-Scenes finetuning

viewformer-cli train finetune-transformer \
    --dataset "{code dataset path}" \
    --codebook-model "{codebook model checkpoint}" \
    --localization-weight 5 \
    --pose-multiplier 5. \
    --batch-size 40 \
    --fp16 \
    --learning-rate 1e-5 \
    --job-dir .  \
    --total-steps 10000 \
    --epochs 10 \
    --checkpoint "{interiornet transformer model checkpoint}"

Here {interiornet transformer model checkpoint} is the path to the InteriorNet checkpoint (usually ending with weights.model.099-last).

ShapeNet finetuning

viewformer-cli train finetune-transformer \
    --dataset "{cars code dataset path},{chairs code dataset path}" \
    --codebook-model "{codebook model checkpoint}" \
    --localization-weight 1 \
    --pose-multiplier 1 \
    --n-loss-skip 1 \
    --sequence-size 4 \
    --batch-size 64 \
    --learning-rate 1e-4 \
    --gradient-clip-val 1 \
    --job-dir .  \
    --total-steps 100000 \
    --epochs 100 \
    --weight-decay 0.05 \
    --checkpoint "{interiornet transformer model checkpoint}"

Here {interiornet transformer model checkpoint} is the path to the InteriorNet checkpoint (usually ending with weights.model.099-last).

SM7 training

viewformer-cli train transformer \
    --dataset "{code dataset path}" \
    --codebook-model "{codebook model checkpoint}" \
    --sequence-size 6 \
    --n-loss-skip 1 \
    --batch-size 128 \
    --fp16 \
    --total-steps 120000 \
    --localization-weight "cosine(0,1,120000)" \
    --learning-rate 1e-4 \
    --weight-decay 0.01 \
    --job-dir . \
    --pose-multiplier 0.2

You can safely replace the cosine schedule for localization weight with a constant term.

Evaluation

Codebook evaluation

In order to evaluate the codebook model, run the following:

viewformer-cli evaluate codebook \
    --codebook-model "{codebook model checkpoint}" \
    --loader-path "{dataset path}" \
    --loader dataset \
    --loader-split test \
    --batch-size 64 \
    --image-size 128 \
    --num-store-images 0 \
    --num-eval-images 1000 \
    --job-dir . 

Note that --image-size argument controls the image size used for computing the metrics. You can change it to a different value.

General transformer evaluation

In order to evaluate the transformer model, run the following:

viewformer-cli evaluate transformer \
    --codebook-model "{codebook model checkpoint}" \
    --transformer-model "{transformer model checkpoint}" \
    --loader-path "{dataset path}" \
    --loader dataset \
    --loader-split test \
    --batch-size 1 \
    --image-size 128 \
    --job-dir . \
    --num-eval-sequences 1000

Optionally, you can use --sequence-size to control the context size used for evaluation. Note that --image-size argument controls the image size used for computing the metrics. You can change it to a different value.

Transformer evaluation with different context sizes

In order to evaluate the transformer model with multiple context sizes, run the following:

viewformer-cli evaluate transformer-multictx \
    --codebook-model "{codebook model checkpoint}" \
    --transformer-model "{transformer model checkpoint}" \
    --loader-path "{dataset path}" \
    --loader dataset \
    --loader-split test \
    --batch-size 1 \
    --image-size 128 \
    --job-dir . \
    --num-eval-sequences 1000

Note that --image-size argument controls the image size used for computing the metrics. You can change it to a different value.

CO3D evaluation

In order to evaluate the transformer model on the CO3D dataset, run the following:

viewformer-cli evaluate \
    --codebook-model "{codebook model checkpoint}" \
    --transformer-model "{transformer model checkpoint}" \
    --path {original CO3D root}
    --job-dir . 

7-Scenes evaluation

In order to evaluate the transformer model on the 7-Scenes dataset, run the following:

viewformer-cli evaluate 7scenes \
    --codebook-model "{codebook model checkpoint}" \
    --transformer-model "{transformer model checkpoint}" \
    --path {original 7-Scenes root}
    --batch-size 1
    --job-dir .
    --num-store-images 0
    --top-n-matched-images 10
    --image-match-map {path to top10 matched images}

You can change --top-n-matched-images to 0 if you don't want to use top 10 closest images in the context. {path to top10 matched images} as a path to the file containing the map between most similar images from the test and the train sets. Each line is in the format {relative test image path} {relative train image path}.

Thanks

We would like to express our sincere gratitude to the authors of the following repositories, that we used in our code:

Issues
  • Some questions about the paper

    Some questions about the paper

    Hello and thanks for sharing this great work!

    I would like to ask some questions, since I'm interested in using this work to extract meshes of the object for unseen instances of a seen (during training) category

    • First of all: is this method capable of retrieving the 3D model from a single view within the same category? (Testing on instances not seen during the training)

    • Is the extracted 3D model with the correct scale?

    • Is there any method in this repo to generate and export mesh as .obj/.ply ?

    Thanks in advance

    opened by AlbertoRemus 2
Owner
Jonáš Kulhánek
Jonáš Kulhánek
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Implementation

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Implementation This project attempted to implement the paper Putting NeRF on a

null 238 Jun 6, 2022
An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

Achraf Rahouti 3 Nov 30, 2021
Official Repo for ICCV2021 Paper: Learning to Regress Bodies from Images using Differentiable Semantic Rendering

[ICCV2021] Learning to Regress Bodies from Images using Differentiable Semantic Rendering Getting Started DSR has been implemented and tested on Ubunt

Sai Kumar Dwivedi 79 Jun 12, 2022
(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters

NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min

Active Vision Laboratory 329 Jun 20, 2022
Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters

[Unofficial code-base] NeRF--: Neural Radiance Fields Without Known Camera Parameters [ Project | Paper | Official code base ] ⬅️ Thanks the original

Jianfei Guo 186 Jun 20, 2022
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields.

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF implementation. Contact Jon Barron if you encounter any issues.

Google 479 Jun 27, 2022
This repository contains a PyTorch implementation of "AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis".

AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis | Project Page | Paper | PyTorch implementation for the paper "AD-NeRF: Audio

null 423 Jun 29, 2022
Code release for DS-NeRF (Depth-supervised Neural Radiance Fields)

Depth-supervised NeRF: Fewer Views and Faster Training for Free Project | Paper | YouTube Pytorch implementation of our method for learning neural rad

null 357 Jun 20, 2022
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

NeRF-pytorch NeRF (Neural Radiance Fields) is a method that achieves state-of-the-art results for synthesizing novel views of complex scenes. Here are

Yen-Chen Lin 2.2k Jun 26, 2022
D-NeRF: Neural Radiance Fields for Dynamic Scenes

D-NeRF: Neural Radiance Fields for Dynamic Scenes [Project] [Paper] D-NeRF is a method for synthesizing novel views, at an arbitrary point in time, of

Albert Pumarola 193 Jun 27, 2022
Code release for NeRF (Neural Radiance Fields)

NeRF: Neural Radiance Fields Project Page | Video | Paper | Data Tensorflow implementation of optimizing a neural representation for a single scene an

null 5.2k Jun 26, 2022
Pytorch implementation for A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose

A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose Paper | Website | Data A-NeRF: Articulated Neural Radiance F

Shih-Yang Su 138 Jun 25, 2022
A minimal TPU compatible Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

NeRF Minimal Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Result of Tiny-NeRF RGB Depth

Soumik Rakshit 9 Mar 28, 2022
Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

Semantic-NeRF: Semantic Neural Radiance Fields Project Page | Video | Paper | Data In-Place Scene Labelling and Understanding with Implicit Scene Repr

Shuaifeng Zhi 131 Jun 21, 2022
Point-NeRF: Point-based Neural Radiance Fields

Point-NeRF: Point-based Neural Radiance Fields Project Sites | Paper | Primary c

Qiangeng Xu 413 Jun 27, 2022
Rendering color and depth images for ShapeNet models.

Color & Depth Renderer for ShapeNet This library includes the tools for rendering multi-view color and depth images of ShapeNet models. Physically bas

Yinyu Nie 28 May 24, 2022
This repository contains the source code for the paper "DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks",

DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks Project Page | Video | Presentation | Paper | Data L

Facebook Research 233 Jun 14, 2022
pixelNeRF: Neural Radiance Fields from One or Few Images

pixelNeRF: Neural Radiance Fields from One or Few Images Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa UC Berkeley arXiv: http://arxiv.org/abs/2

Alex Yu 837 Jun 30, 2022
Few-NERD: Not Only a Few-shot NER Dataset

Few-NERD: Not Only a Few-shot NER Dataset This is the source code of the ACL-IJCNLP 2021 paper: Few-NERD: A Few-shot Named Entity Recognition Dataset.

THUNLP 281 Jun 28, 2022