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Infrared thermography has been widely used in several domains to capture and measure temperature distributions across surfaces and objects. This methodology can be further expanded to 3D applications if the spatial distribution of the…

Computer Vision and Pattern Recognition · Computer Science 2024-01-17 Jincheng Zhang , Kevin Brink , Andrew R Willis

Structure from Motion (SfM) refers to the problem of recovering both structure (i.e., 3D coordinates of points in the scene) and motion (i.e., camera matrices) starting from point correspondences in multiple images. It has attracted…

Computer Vision and Pattern Recognition · Computer Science 2025-05-22 Federica Arrigoni

We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint matching, which traditional SfM methods often rely on, limits both accuracy and…

Computer Vision and Pattern Recognition · Computer Science 2026-01-29 JongMin Lee , Sungjoo Yoo

Multi-camera systems are increasingly vital in the environmental perception of autonomous vehicles and robotics. Their physical configuration offers inherent fixed relative pose constraints that benefit Structure-from-Motion (SfM). However,…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Peilin Tao , Hainan Cui , Diantao Tu , Shuhan Shen

Creating 3D models through the Structure from Motion technique is a recognized, efficient, cost-effective structural monitoring strategy. This technique is applied in several engineering fields, particularly for creating models of large…

Computer Vision and Pattern Recognition · Computer Science 2024-09-25 Francisco Roza de Moraes , Irineu da Silva

We present NeRSP, a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. Reflective surface reconstruction is extremely challenging as specular reflections are view-dependent and thus violate the…

Computer Vision and Pattern Recognition · Computer Science 2024-06-12 Yufei Han , Heng Guo , Koki Fukai , Hiroaki Santo , Boxin Shi , Fumio Okura , Zhanyu Ma , Yunpeng Jia

Achieving high-fidelity 3D surface reconstruction while preserving fine details remains challenging, especially in the presence of materials with complex reflectance properties and without a dense-view setup. In this paper, we introduce a…

Computer Vision and Pattern Recognition · Computer Science 2026-01-13 Robin Bruneau , Baptiste Brument , Yvain Quéau , Jean Mélou , François Bernard Lauze , Jean-Denis Durou , Lilian Calvet

Structure-from-Motion approaches could be broadly divided into two classes: incremental and global. While incremental manner is robust to outliers, it suffers from error accumulation and heavy computation load. The global manner has the…

Computer Vision and Pattern Recognition · Computer Science 2018-03-26 Hainan Cui , Shuhan Shen , Xiang Gao , Zhanyi Hu

Structure from Motion (SfM) is a critical task in computer vision, aiming to recover the 3D scene structure and camera motion from a sequence of 2D images. The recent pose-only imaging geometry decouples 3D coordinates from camera poses and…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Xinrui Li , Qi Cai , Yuanxin Wu

Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM)…

Computer Vision and Pattern Recognition · Computer Science 2023-03-22 Yu-Lun Liu , Chen Gao , Andreas Meuleman , Hung-Yu Tseng , Ayush Saraf , Changil Kim , Yung-Yu Chuang , Johannes Kopf , Jia-Bin Huang

This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-S$^2$fM, which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and…

Computer Vision and Pattern Recognition · Computer Science 2023-03-28 Yuxi Xiao , Nan Xue , Tianfu Wu , Gui-Song Xia

Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete ground truth, or lack important sensor data, such as depth and…

Computer Vision and Pattern Recognition · Computer Science 2023-01-04 Janne Mustaniemi , Juho Kannala , Esa Rahtu , Li Liu , Janne Heikkilä

Structure-from-Motion (SfM) is a fundamental technique for recovering camera poses and scene structure from multi-view imagery, serving as a critical upstream component for applications ranging from 3D reconstruction to modern neural scene…

Computer Vision and Pattern Recognition · Computer Science 2026-03-12 Jiankun Zhong , Zitong Zhan , Quankai Gao , Ziyu Chen , Haozhe Lou , Jiageng Mao , Ulrich Neumann , Chen Wang , Yue Wang

This article describes a technique to augment a typical RGBD sensor by integrating depth estimates obtained via Structure-from-Motion (SfM) with sensor depth measurements. Limitations in the RGBD depth sensing technology prevent capturing…

Computer Vision and Pattern Recognition · Computer Science 2021-03-12 Akash Chandrashekar , John Papadakis , Andrew Willis , Jamie Gantert

We propose a new structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful detection of repeatable keypoints across multiple views as…

Computer Vision and Pattern Recognition · Computer Science 2023-06-28 Xingyi He , Jiaming Sun , Yifan Wang , Sida Peng , Qixing Huang , Hujun Bao , Xiaowei Zhou

Streaming feed-forward 3D reconstruction enables real-time joint estimation of scene geometry and camera poses from RGB images. However, without explicit dynamic reasoning, streaming models can be affected by moving objects, causing…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Feiran Wang , Zezhou Shang , Gaowen Liu , Yan Yan

Efficient and accurate camera pose estimation forms the foundational requirement for dense reconstruction in autonomous navigation, robotic perception, and virtual simulation systems. This paper addresses the challenge via cuSfM, a…

Computer Vision and Pattern Recognition · Computer Science 2025-10-20 Jingrui Yu , Jun Liu , Kefei Ren , Joydeep Biswas , Rurui Ye , Keqiang Wu , Chirag Majithia , Di Zeng

Active 3D measurement, especially structured light (SL) has been widely used in various fields for its robustness against textureless or equivalent surfaces by low light illumination. In addition, reconstruction of large scenes by moving…

Computer Vision and Pattern Recognition · Computer Science 2024-10-22 Kazuto Ichimaru , Diego Thomas , Takafumi Iwaguchi , Hiroshi Kawasaki

Rolling-shutter (RS) cameras are ubiquitous, but RS SfM (structure-from-motion) has not been fully solved yet. This work suggests an approach to remedy this: We characterize RS single-view geometry of observed world points or lines.…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Sofía Errázuriz Muñoz , Kim Kiehn , Petr Hruby , Kathlén Kohn

Current multi-view 3D reconstruction methods rely on accurate camera calibration and pose estimation, requiring complex and time-intensive pre-processing that hinders their practical deployment. To address this challenge, we introduce…

Graphics · Computer Science 2025-08-07 Haodong Zhu , Changbai Li , Yangyang Ren , Zichao Feng , Xuhui Liu , Hanlin Chen , Xiantong Zhen , Baochang Zhang