Volsdf - Volume Rendering of Neural Implicit Surfaces

Related tags

Deep Learningvolsdf
Overview

Volume Rendering of Neural Implicit Surfaces

Project Page | Paper | Data

This repository contains an implementation for the NeurIPS 2021 paper:
Volume Rendering of Neural Implicit Surfaces
Lior Yariv1, Jiatao Gu2, Yoni Kasten1, Yaron Lipman1,2
1Weizmann Institute of Science, 2Facebook AI Research

The paper introduce VolSDF: a volume rendering framework for implicit neural surfaces, allowing to learn high fidelity geometry from a sparse set of input images.

Setup

Installation Requirmenets

The code is compatible with python 3.8 and pytorch 1.7. In addition, the following packages are required:
numpy, pyhocon, plotly, scikit-image, trimesh, imageio, opencv, torchvision.

You can create an anaconda environment called volsdf with the required dependencies by running:

conda env create -f environment.yml
conda activate volsdf

Data

We apply our multiview surface reconstruction model to real 2D images from two datasets: DTU and BlendedMVS. The selected scans data evaluated in the paper can be downloaded using:

bash data/download_data.sh 

For more information on the data convention and how to run VolSDF on a new data please have a look at data convention.

Usage

Multiview 3D reconstruction

For training VolSDF run:

cd ./code
python training/exp_runner.py --conf ./confs/dtu.conf --scan_id SCAN_ID

where SCAN_ID is the id of the scene to reconstruct.

To run on the BlendedMVS dataset, which have more complex background, use --conf ./confs/bmvs.conf.

Evaluation

To produce the meshed surface and renderings, run:

cd ./code
python evaluation/eval.py  --conf ./confs/dtu.conf --scan_id SCAN_ID --checkpoint CHECKPOINT [--eval_rendering]

where CHECKPOINT is the epoch you wish to evaluate or 'latest' if you wish to take the most recent epoch. Turning on --eval_rendering will further produce and evaluate PSNR of train image reconstructions.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{yariv2021volume,
  title={Volume rendering of neural implicit surfaces},
  author={Yariv, Lior and Gu, Jiatao and Kasten, Yoni and Lipman, Yaron},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}
Owner
Lior Yariv
Lior Yariv
Official Implementation of Few-shot Visual Relationship Co-localization

VRC Official implementation of the Few-shot Visual Relationship Co-localization (ICCV 2021) paper project page | paper Requirements Use python = 3.8.

22 Oct 13, 2022
Codebase for testing whether hidden states of neural networks encode discrete structures.

structural-probes Codebase for testing whether hidden states of neural networks encode discrete structures. Based on the paper A Structural Probe for

John Hewitt 349 Dec 17, 2022
Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)

NDQ: Learning Nearly Decomposable Value Functions with Communication Minimization Note This codebase accompanies paper Learning Nearly Decomposable Va

Tonghan Wang 69 Nov 26, 2022
Using VapourSynth with super resolution models and speeding them up with TensorRT.

VSGAN-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Using NVIDIA/Torch-TensorRT combined wi

111 Jan 05, 2023
This is the code related to "Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation" (ICCV 2021).

Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation This is the code relat

39 Sep 23, 2022
PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition The unofficial code of CDistNet. Now, we ha

25 Jul 20, 2022
YOLOv4-v3 Training Automation API for Linux

This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our

BMW TechOffice MUNICH 626 Dec 31, 2022
Galileo library for large scale graph training by JD

近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提

JD Galileo Team 128 Nov 29, 2022
Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection This project provides an implementation for "LLA: Loss-aware Label Assignment for Dens

35 Dec 06, 2022
Analysis of Antarctica sequencing samples contaminated with SARS-CoV-2

Analysis of SARS-CoV-2 reads in sequencing of 2018-2019 Antarctica samples in PRJNA692319 The samples analyzed here are described in this preprint, wh

Jesse Bloom 4 Feb 09, 2022
[ICML 2021] Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data

Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data This repo provides the source code & data of our paper: Break-It-Fix-It: Unsupervised

Michihiro Yasunaga 86 Nov 30, 2022
Rl-quickstart - Reinforcement Learning Quickstart

Reinforcement Learning Quickstart To get setup with the repository, git clone ht

UCLA DataRes 3 Jun 16, 2022
RLHive: a framework designed to facilitate research in reinforcement learning.

RLHive is a framework designed to facilitate research in reinforcement learning. It provides the components necessary to run a full RL experiment, for both single agent and multi agent environments.

88 Jan 05, 2023
SimDeblur is a simple framework for image and video deblurring, implemented by PyTorch

SimDeblur (Simple Deblurring) is an open source framework for image and video deblurring toolbox based on PyTorch, which contains most deep-learning based state-of-the-art deblurring algorithms. It i

220 Jan 07, 2023
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
Prososdy Morph: A python library for manipulating pitch and duration in an algorithmic way, for resynthesizing speech.

ProMo (Prosody Morph) Questions? Comments? Feedback? Chat with us on gitter! A library for manipulating pitch and duration in an algorithmic way, for

Tim 71 Jan 02, 2023
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation.

Training Script for Reuse-VOS This code implementation of CVPR 2021 paper : Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Vi

HYOJINPARK 22 Jan 01, 2023
Image Super-Resolution by Neural Texture Transfer

SRNTT: Image Super-Resolution by Neural Texture Transfer Tensorflow implementation of the paper Image Super-Resolution by Neural Texture Transfer acce

Zhifei Zhang 413 Nov 30, 2022
PyTorch implementation of our paper: Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition

Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition, arxiv This is a PyTorch implementation of our paper. 1. Re

DamoCV 11 Nov 19, 2022
An official implementation of the paper Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

Sequence Feature Alignment (SFA) By Wen Wang, Yang Cao, Jing Zhang, Fengxiang He, Zheng-jun Zha, Yonggang Wen, and Dacheng Tao This repository is an o

WangWen 79 Dec 24, 2022