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.

Implementation of popular bandit algorithms in batch environments.

batch-bandits Implementation of popular bandit algorithms in batch environments. Source code to our paper "The Impact of Batch Learning in Stochastic

Danil Provodin 2 Sep 11, 2022
HINet: Half Instance Normalization Network for Image Restoration

HINet: Half Instance Normalization Network for Image Restoration Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, Chengpeng Chen Paper: https://arxiv.org

303 Dec 31, 2022
We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Overview This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which

NEU-StatsML-Research 21 Sep 08, 2022
Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.

Visual Transformer for Facial Emotion Recognition (FER) This project has the aim to build an efficient Visual Transformer for the Facial Emotion Recog

Mario Sessa 8 Dec 12, 2022
Minimal deep learning library written from scratch in Python, using NumPy/CuPy.

SmallPebble Project status: experimental, unstable. SmallPebble is a minimal/toy automatic differentiation/deep learning library written from scratch

Sidney Radcliffe 92 Dec 30, 2022
Madanalysis5 - A package for event file analysis and recasting of LHC results

Welcome to MadAnalysis 5 Outline What is MadAnalysis 5? Requirements Downloading

MadAnalysis 15 Jan 01, 2023
SGPT: Multi-billion parameter models for semantic search

SGPT: Multi-billion parameter models for semantic search This repository contains code, results and pre-trained models for the paper SGPT: Multi-billi

Niklas Muennighoff 182 Dec 29, 2022
Approaches to modeling terrain and maps in python

topography 🌎 Contains different approaches to modeling terrain and topographic-style maps in python Features Inverse Distance Weighting (IDW) A given

John Gutierrez 1 Aug 10, 2022
TensorFlow CNN for fast style transfer

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! It takes 100ms on a 2015 Titan X to style t

1 Dec 14, 2021
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains

Open-Domain Question-Answering for COVID-19 and Other Emergent Domains This repository contains the source code for an end-to-end open-domain question

7 Sep 27, 2022
Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

Robotic Systems Lab - Legged Robotics at ETH Zürich 68 Dec 29, 2022
Compare neural networks by their feature similarity

PyTorch Model Compare A tiny package to compare two neural networks in PyTorch. There are many ways to compare two neural networks, but one robust and

Anand Krishnamoorthy 181 Jan 04, 2023
PyTorch implementation of hand mesh reconstruction described in CMR and MobRecon.

Hand Mesh Reconstruction Introduction This repo is the PyTorch implementation of hand mesh reconstruction described in CMR and MobRecon. Update 2021-1

Xingyu Chen 236 Dec 29, 2022
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Subspace Adversarial Training Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However,

15 Sep 02, 2022
A Multi-modal Model Chinese Spell Checker Released on ACL2021.

ReaLiSe ReaLiSe is a multi-modal Chinese spell checking model. This the office code for the paper Read, Listen, and See: Leveraging Multimodal Informa

DaDa 106 Dec 29, 2022
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization

University1652-Baseline [Paper] [Slide] [Explore Drone-view Data] [Explore Satellite-view Data] [Explore Street-view Data] [Video Sample] [中文介绍] This

Zhedong Zheng 335 Jan 06, 2023
Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

Interaction-Network-Pytorch Pytorch Implementraion of Interaction Networks for Learning about Objects, Relations and Physics. Interaction Network is a

117 Nov 05, 2022
A developer interface for creating Chat AIs for the Chai app.

ChaiPy A developer interface for creating Chat AIs for the Chai app. Usage Local development A quick start guide is available here, with a minimal exa

Chai 28 Dec 28, 2022
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation Winner method of the ICCV-2021 SemKITTI-DVPS Challenge. [arxiv] [

Yuan Haobo 38 Jan 03, 2023
Automated Evidence Collection for Fake News Detection

Automated Evidence Collection for Fake News Detection This is the code repo for the Automated Evidence Collection for Fake News Detection paper accept

Mrinal Rawat 2 Apr 12, 2022