Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

Related tags

Deep Learningsrf
Overview

Stereo Radiance Fields

Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

Teaser

Paper - Supplementaty - Video - Project Website - Arxiv - If you find our project useful, please cite us. Citation (Bibtex)

Install

A linux system with python environment manager conda and a full and system wide installation of the CUDA Toolkit 10.1 is required for the project (the latter only for compilation of the torchsearchsorted library).

The following commands clone the repo on your machine and install an environment, "srf", containing all dependencies.

git clone ADD LINK
cd SRF_git
conda env create -f srf_env.yml

Please close the terminal session at this point, and reopen it at the same location. This is done to ensure conda correctly loads all packages.

conda activate srf
pip install torchsearchsorted/

Data Setup

With the next commands the DTU MVS dataset is downloaded and put in place.

wget http://roboimagedata2.compute.dtu.dk/data/MVS/Rectified.zip -P data/
unzip data/Rectified.zip -d data/
mv data/Rectified/* data/DTU_MVS
rmdir data/Rectified

Quick Start with Pretrained Model

To synthesise novel views use the following command

python generator.py --config configs/finetune_scan23.txt --video --render_factor 8 --generate_specific_samples scan23 --fixed_batch 1 --ft_path checkpoint.tar --gen_pose 0

where --config specifies the path to the experiment configuration and --gen_pose is the frame number from 0-55 (including both).

Training

Coming soon.

Contact

For questions and comments please contact Julian Chibane via mail.

License

Copyright (c) 2021 Julian Chibane, Max-Planck-Gesellschaft

By downloading and using this code you agree to the terms in the LICENSE.

You agree to cite the Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes paper in documents and papers that report on research using this software or the manuscript.

Show LICENSE (click to expand) Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software").

The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.

Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes. For commercial inquiries, please see above contact information.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

You understand and agree that the authors are under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Software. The authors nevertheless reserve the right to update, modify, or discontinue the Software at any time.

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

Official implementation of our paper "Learning to Bootstrap for Combating Label Noise"

Learning to Bootstrap for Combating Label Noise This repo is the official implementation of our paper "Learning to Bootstrap for Combating Label Noise

21 Apr 09, 2022
Fuzzer for Linux Kernel Drivers

difuze: Fuzzer for Linux Kernel Drivers This repo contains all the sources (including setup scripts), you need to get difuze up and running. Tested on

seclab 344 Dec 27, 2022
This is an open solution to the Home Credit Default Risk challenge 🏡

Home Credit Default Risk: Open Solution This is an open solution to the Home Credit Default Risk challenge 🏡 . More competitions 🎇 Check collection

minerva.ml 427 Dec 27, 2022
OMAMO: orthology-based model organism selection

OMAMO: orthology-based model organism selection OMAMO is a tool that suggests the best model organism to study a biological process based on orthologo

Dessimoz Lab 5 Apr 22, 2022
hySLAM is a hybrid SLAM/SfM system designed for mapping

HySLAM Overview hySLAM is a hybrid SLAM/SfM system designed for mapping. The system is based on ORB-SLAM2 with some modifications and refactoring. Raú

Brian Hopkinson 15 Oct 10, 2022
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Keras当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和fa

Bubbliiiing 31 Nov 15, 2022
The MATH Dataset

Measuring Mathematical Problem Solving With the MATH Dataset This is the repository for Measuring Mathematical Problem Solving With the MATH Dataset b

Dan Hendrycks 267 Dec 26, 2022
Open-source code for Generic Grouping Network (GGN, CVPR 2022)

Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity Pytorch implementation for "Open-World Instance Segmen

Meta Research 99 Dec 06, 2022
A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM's

sign-language-detection A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM. The project is built for a vocabular

Hashim 4 Feb 06, 2022
Elegy is a framework-agnostic Trainer interface for the Jax ecosystem.

Elegy Elegy is a framework-agnostic Trainer interface for the Jax ecosystem. Main Features Easy-to-use: Elegy provides a Keras-like high-level API tha

435 Dec 30, 2022
Message Passing on Cell Complexes

CW Networks This repository contains the code used for the papers Weisfeiler and Lehman Go Cellular: CW Networks (Under review) and Weisfeiler and Leh

Twitter Research 108 Jan 05, 2023
mmdetection version of TinyBenchmark.

introduction This project is an mmdetection version of TinyBenchmark. TODO list: add TinyPerson dataset and evaluation add crop and merge for image du

34 Aug 27, 2022
The project page of paper: Architecture disentanglement for deep neural networks [ICCV 2021, oral]

This is the project page for the paper: Architecture Disentanglement for Deep Neural Networks, Jie Hu, Liujuan Cao, Tong Tong, Ye Qixiang, ShengChuan

Jie Hu 15 Aug 30, 2022
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)

Skyformer This repository is the official implementation of Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr"om Method (NeurIPS 2021).

Qi Zeng 46 Sep 20, 2022
Code for "Modeling Indirect Illumination for Inverse Rendering", CVPR 2022

Modeling Indirect Illumination for Inverse Rendering Project Page | Paper | Data Preparation Set up the python environment conda create -n invrender p

ZJU3DV 116 Jan 03, 2023
NeRD: Neural Reflectance Decomposition from Image Collections

NeRD: Neural Reflectance Decomposition from Image Collections Project Page | Video | Paper | Dataset Implementation for NeRD. A novel method which dec

Computergraphics (University of Tübingen) 195 Dec 29, 2022
3D ResNets for Action Recognition (CVPR 2018)

3D ResNets for Action Recognition Update (2020/4/13) We published a paper on arXiv. Hirokatsu Kataoka, Tenga Wakamiya, Kensho Hara, and Yutaka Satoh,

Kensho Hara 3.5k Jan 06, 2023
A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning

A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning Website • About • Installation • Using OpenDR

OpenDR 304 Dec 28, 2022
An investigation project for SISR.

SISR-Survey An investigation project for SISR. This repository is an official project of the paper "From Beginner to Master: A Survey for Deep Learnin

Juncheng Li 79 Oct 20, 2022
Source-to-Source Debuggable Derivatives in Pure Python

Tangent Tangent is a new, free, and open-source Python library for automatic differentiation. Existing libraries implement automatic differentiation b

Google 2.2k Jan 01, 2023