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City-scale 3D surface reconstruction from multiview images for downstream 3D simulation, poses highly challenging problems due to the scale and complexity of urban scenes. Existing city-scale 3D reconstruction methods based on NeRF,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Sayan Paul , Sourav Ghosh , Siddharth Katageri , Soumyadip Maity , Sanjana Sinha , Brojeshwar Bhowmick

Scene regression methods, such as VGGT, solve the Structure-from-Motion (SfM) problem by directly regressing camera poses and 3D scene structures from input images. They demonstrate impressive performance in handling images under extreme…

Computer Vision and Pattern Recognition · Computer Science 2025-08-26 Junyuan Deng , Heng Li , Tao Xie , Weiqiang Ren , Qian Zhang , Ping Tan , Xiaoyang Guo

Structure-from-Motion (SfM) is a fundamental 3D vision task for recovering camera parameters and scene geometry from multi-view images. While recent deep learning advances enable accurate Monocular Depth Estimation (MDE) from single images…

Computer Vision and Pattern Recognition · Computer Science 2026-02-24 Shengjie Zhu , Ahmed Abdelkader , Mark J. Matthews , Xiaoming Liu , Wen-Sheng Chu

Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this paper we develop an image processing technique for aiding 3D reconstruction from images acquired in…

Robotics · Computer Science 2021-08-24 Ahalya Ravendran , Mitch Bryson , Donald G. Dansereau

Estimating camera intrinsics and extrinsics is a fundamental problem in computer vision, and while advances in structure-from-motion (SfM) have improved accuracy and robustness, open challenges remain. In this paper, we introduce a robust…

Computer Vision and Pattern Recognition · Computer Science 2025-12-18 Johannes Hägerlind , Bao-Long Tran , Urs Waldmann , Per-Erik Forssén

Multi-view 3D reconstruction, namely, structure-from-motion followed by multi-view stereo, is a fundamental component of 3D computer vision. In general, multi-view 3D reconstruction suffers from an unknown scale ambiguity unless a reference…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Lilika Makabe , Kohei Ashida , Hiroaki Santo , Fumio Okura , Yasuyuki Matsushita

We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental reconstruction: they repeat triangulation of sparse 3D points…

Computer Vision and Pattern Recognition · Computer Science 2024-07-29 Eric Brachmann , Jamie Wynn , Shuai Chen , Tommaso Cavallari , Áron Monszpart , Daniyar Turmukhambetov , Victor Adrian Prisacariu

Long-term environmental monitoring requires the ability to reconstruct and align 3D models across repeated site visits separated by months or years. However, existing Structure-from-Motion (SfM) pipelines implicitly assume near-simultaneous…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Beverley Gorry , Tobias Fischer , Michael Milford , Alejandro Fontan

This work introduces an effective and practical solution to the dense two-view structure from motion (SfM) problem. One vital question addressed is how to mindfully use per-pixel optical flow correspondence between two frames for accurate…

Computer Vision and Pattern Recognition · Computer Science 2023-02-14 Weirong Chen , Suryansh Kumar , Fisher Yu

Several methods have been proposed for large-scale 3D reconstruction from large, unorganized image collections. A large reconstruction problem is typically divided into multiple components which are reconstructed independently using…

Computer Vision and Pattern Recognition · Computer Science 2016-10-13 Rajvi Shah , Aditya Deshpande , P J Narayanan

Visual localization is a critical task in mobile robotics, and researchers are continuously developing new approaches to enhance its efficiency. In this article, we propose a novel approach to improve the accuracy of visual localization…

Computer Vision and Pattern Recognition · Computer Science 2023-10-10 Artem Nenashev , Mikhail Kurenkov , Andrei Potapov , Iana Zhura , Maksim Katerishich , Dzmitry Tsetserukou

A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations. The…

Computer Vision and Pattern Recognition · Computer Science 2021-07-14 Shaifali Parashar , Yuxuan Long , Mathieu Salzmann , Pascal Fua

The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision. Traditional methods have been confined to specific rigid objects, such as those in Perspective-n-Point (PnP)…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Mosam Dabhi , Laszlo A. Jeni , Simon Lucey

Over the last decades, ample achievements have been made on Structure from motion (SfM). However, the vast majority of them basically work in an offline manner, i.e., images are firstly captured and then fed together into a SfM pipeline for…

Computer Vision and Pattern Recognition · Computer Science 2024-02-15 Zongqian Zhan , Rui Xia , Yifei Yu , Yibo Xu , Xin Wang

Novel view synthesis (NVS) approaches such as NeRFs or 3DGS can produce photo-realistic 3D scene representation from a set of images with known extrinsic and intrinsic parameters. The necessary camera poses and calibrations are typically…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Jhacson Meza , Martin R. Oswald , Torsten Sattler

Acquiring 3D geometry of real world objects has various applications in 3D digitization, such as navigation and content generation in virtual environments. Image remains one of the most popular media for such visual tasks due to its…

Computer Vision and Pattern Recognition · Computer Science 2017-01-26 Shuai Du , Youyi Zheng

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with…

Computer Vision and Pattern Recognition · Computer Science 2021-06-03 Shaifali Parashar , Adrien Bartoli , Daniel Pizarro

In this paper, we present a complete refractive Structure-from-Motion (RSfM) framework for underwater 3D reconstruction using refractive camera setups (for both, flat- and dome-port underwater housings). Despite notable achievements in…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Mengkun She , Felix Seegräber , David Nakath , Kevin Köser

Reliable displacement measurement is fundamental for structural health monitoring and digital engineering workflows, as it provides direct structural response information. Vision-based measurement has emerged as a promising approach for…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Qingyu Xian , Hao Cheng , Berend Jan van der Zwaag , Rolands Kromanis , Ozlem Durmaz Incel

Both self-supervised depth estimation and Structure-from-Motion (SfM) recover scene depth from RGB videos. Despite sharing a similar objective, the two approaches are disconnected. Prior works of self-supervision backpropagate losses…

Computer Vision and Pattern Recognition · Computer Science 2024-08-08 Shengjie Zhu , Xiaoming Liu
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