Pixel-Perfect Structure-from-Motion with Featuremetric Refinement (ICCV 2021, Oral)

Overview

Pixel-Perfect Structure-from-Motion (ICCV 2021 Oral)

We introduce a framework that improves the accuracy of Structure-from-Motion by refining keypoints, camera poses, and 3D points using the direct alignment of deep features. It is presented in our paper:

This repository will host the code to run and evaluate our refinement. Please subscribe to this issue if you wish to be notified of the code release.

Abstract

Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction. The classical image matching paradigm detects keypoints per-image once and for all, which can yield poorly-localized features and propagate large errors to the final geometry. In this paper, we refine two key steps of structure-from-motion by a direct alignment of low-level image information from multiple views: we first adjust the initial keypoint locations prior to any geometric estimation, and subsequently refine points and camera poses as a post-processing. This refinement is robust to large detection noise and appearance changes, as it optimizes a featuremetric error based on dense features predicted by a neural network. This significantly improves the accuracy of camera poses and scene geometry for a wide range of keypoint detectors, challenging viewing conditions, and off-the-shelf deep features. Our system easily scales to large image collections, enabling pixel-perfect crowd-sourced localization at scale. Our code will be publicly available at as an add-on to the popular SfM software COLMAP.

BibTex Citation

Please consider citing our work if you use any code from this repo or ideas presented in the paper:

@inproceedings{lindenberger2021pixsfm,
  author    = {Philipp Lindenberger and
               Paul-Edouard Sarlin and
               Viktor Larsson and
               Marc Pollefeys},
  title     = {{Pixel-Perfect Structure-from-Motion with Featuremetric Refinement}},
  booktitle = {ICCV},
  year      = {2021},
}
Owner
Computer Vision and Geometry Lab
Computer Vision and Geometry Lab
End-to-end image segmentation kit based on PaddlePaddle.

English | 简体中文 PaddleSeg PaddleSeg has released the new version including the following features: Our team won the 6.2k Jan 02, 2023

TRIQ implementation

TRIQ Implementation TF-Keras implementation of TRIQ as described in Transformer for Image Quality Assessment. Installation Clone this repository. Inst

Junyong You 115 Dec 30, 2022
This repository implements Douzero's interface to IGCA.

douzero-interface-for-ICGA This repository implements Douzero's interface to ICGA. ./douzero: This directory stores Doudizhu AI projects. ./interface:

zhanggenjin 4 Aug 07, 2022
A multi-scale unsupervised learning for deformable image registration

A multi-scale unsupervised learning for deformable image registration Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu and Baochang Zha

ShuweiShao 2 Apr 13, 2022
Code for the paper "Next Generation Reservoir Computing"

Next Generation Reservoir Computing This is the code for the results and figures in our paper "Next Generation Reservoir Computing". They are written

OSU QuantInfo Lab 105 Dec 20, 2022
Extending JAX with custom C++ and CUDA code

Extending JAX with custom C++ and CUDA code This repository is meant as a tutorial demonstrating the infrastructure required to provide custom ops in

Dan Foreman-Mackey 237 Dec 23, 2022
Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification tasks

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

Phil Wang 90 Nov 24, 2022
SAPIEN Manipulation Skill Benchmark

ManiSkill Benchmark SAPIEN Manipulation Skill Benchmark (abbreviated as ManiSkill, pronounced as "Many Skill") is a large-scale learning-from-demonstr

Hao Su's Lab, UCSD 107 Jan 08, 2023
Codebase for "ProtoAttend: Attention-Based Prototypical Learning."

Codebase for "ProtoAttend: Attention-Based Prototypical Learning." Authors: Sercan O. Arik and Tomas Pfister Paper: Sercan O. Arik and Tomas Pfister,

47 2 May 17, 2022
KoRean based ELECTRA pre-trained models (KR-ELECTRA) for Tensorflow and PyTorch

KoRean based ELECTRA (KR-ELECTRA) This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computa

12 Jun 03, 2022
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
[ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang

Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated [talk] [poster] [appendix] Yuning You, Tianlong C

Shen Lab at Texas A&M University 80 Nov 23, 2022
Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021)

Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021) Alexey Nekrasov*, Jonas Schult*, Or Litany, Bastian Leibe, Francis Engelmann Mix3D is

Alexey Nekrasov 189 Dec 26, 2022
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

CapsGNN ⠀⠀ A PyTorch implementation of Capsule Graph Neural Network (ICLR 2019). Abstract The high-quality node embeddings learned from the Graph Neur

Benedek Rozemberczki 1.2k Jan 02, 2023
Listing arxiv - Personalized list of today's articles from ArXiv

Personalized list of today's articles from ArXiv Print and/or send to your gmail

Lilianne Nakazono 5 Jun 17, 2022
Аналитика доходности инвестиционного портфеля в Тинькофф брокере

Аналитика доходности инвестиционного портфеля Тиньков Видео на YouTube Для работы скрипта нужно установить три переменных окружения: export TINKOFF_TO

Alexey Goloburdin 64 Dec 17, 2022
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Tong WU 304 Dec 22, 2022
Fast, differentiable sorting and ranking in PyTorch

Torchsort Fast, differentiable sorting and ranking in PyTorch. Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.)

Teddy Koker 655 Jan 04, 2023
Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

Obs-Causal-Q-Network AAAI 2022 - Training a Resilient Q-Network against Observational Interference Preprint | Slides | Colab Demo | Environment Setup

23 Nov 21, 2022
Code in conjunction with the publication 'Contrastive Representation Learning for Hand Shape Estimation'

HanCo Dataset & Contrastive Representation Learning for Hand Shape Estimation Code in conjunction with the publication: Contrastive Representation Lea

Computer Vision Group, Albert-Ludwigs-Universität Freiburg 38 Dec 13, 2022