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We explain theoretically how to reconstruct the 3D scene from successive frames in order to see the video in 3D. To do this, features, associated to moving rigid objects in 3D, are extracted in frames and matched. The vanishing point…

Computer Vision and Pattern Recognition · Computer Science 2020-07-30 Slimane Larabi

As a core step in structure-from-motion and SLAM, robust feature detection and description under challenging scenarios such as significant viewpoint changes remain unresolved despite their ubiquity. While recent works have identified the…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Gonglin Chen , Tianwen Fu , Haiwei Chen , Wenbin Teng , Hanyuan Xiao , Yajie Zhao

Predicting the pose of objects from a single image is an important but difficult computer vision problem. Methods that predict a single point estimate do not predict the pose of objects with symmetries well and cannot represent uncertainty.…

Computer Vision and Pattern Recognition · Computer Science 2023-02-28 David M. Klee , Ondrej Biza , Robert Platt , Robin Walters

The perspective camera and the isometric surface prior have recently gathered increased attention for Non-Rigid Structure-from-Motion (NRSfM). Despite the recent progress, several challenges remain, particularly the computational complexity…

Computer Vision and Pattern Recognition · Computer Science 2018-08-14 Thomas Probst , Danda Pani Paudel , Ajad Chhatkuli , Luc Van Gool

Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured. By contrast, recovering the 3D shape of texture-less surfaces remains an…

Computer Vision and Pattern Recognition · Computer Science 2018-07-30 Jan Bednařík , Pascal Fua , Mathieu Salzmann

We propose a new recursive method for simultaneous estimation of both the pose and the shape of a three-dimensional extended object. The key idea of the presented method is to represent the shape of the object using spherical harmonics,…

Robotics · Computer Science 2020-12-29 Gerhard Kurz , Florian Faion , Florian Pfaff , Antonio Zea , Uwe D. Hanebeck

In multi-view human body capture systems, the recovered 3D geometry or even the acquired imagery data can be heavily corrupted due to occlusions, noise, limited field of- view, etc. Direct estimation of 3D pose, body shape or motion on…

Computer Vision and Pattern Recognition · Computer Science 2018-02-02 Zhong Li , Yu Ji , Wei Yang , Jinwei Ye , Jingyi Yu

In the past decades, feature-learning-based 3D shape retrieval approaches have been received widespread attention in the computer graphic community. These approaches usually explored the hand-crafted distance metric or conventional distance…

Computer Vision and Pattern Recognition · Computer Science 2019-01-11 Huibing Wang , Haohao Li , Xianping Fu

Deformable Parts Models and Convolutional Networks each have achieved notable performance in object detection. Yet these two approaches find their strengths in complementary areas: DPMs are well-versed in object composition, modeling…

Computer Vision and Pattern Recognition · Computer Science 2014-11-20 Li Wan , David Eigen , Rob Fergus

We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent…

Computer Vision and Pattern Recognition · Computer Science 2020-12-02 Qihao Liu , Weichao Qiu , Weiyao Wang , Gregory D. Hager , Alan L. Yuille

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior…

Computer Vision and Pattern Recognition · Computer Science 2017-09-18 Zorah Lähner , Matthias Vestner , Amit Boyarski , Or Litany , Ron Slossberg , Tal Remez , Emanuele Rodolà , Alex Bronstein , Michael Bronstein , Ron Kimmel , Daniel Cremers

In this paper we consider 3D axis-symmetric (AS) object reconstruction from single cone-beam x-ray projection data. Traditional x-ray CT fails to capture fleeting state of material due to the long time for data acquisition at all angles.…

Numerical Analysis · Mathematics 2019-12-18 Shousheng Luo , Ruyue Meng , Suhua Wei , Jianfeng Cai , Xuecheng Tai , Yang Wang

Accurate modelling of object deformations is crucial for a wide range of robotic manipulation tasks, where interacting with soft or deformable objects is essential. Current methods struggle to generalise to unseen forces or adapt to new…

Robotics · Computer Science 2025-05-20 Sean M. V. Collins , Brendan Tidd , Mahsa Baktashmotlagh , Peyman Moghadam

3D Reconstruction of moving articulated objects without additional information about object structure is a challenging problem. Current methods overcome such challenges by employing category-specific skeletal models. Consequently, they do…

Computer Vision and Pattern Recognition · Computer Science 2024-01-18 Hao Zhang , Fang Li , Samyak Rawlekar , Narendra Ahuja

We describe a set of tools for analyzing, visualizing, and assessing architectural/construction progress with unordered photo collections and 3D building models. With our interface, a user guides the registration of the model in one of the…

Graphics · Computer Science 2019-12-30 Kevin Karsch , Mani Golparvar-Fard , David Forsyth

Much progress has been made in the supervised learning of 3D reconstruction of rigid objects from multi-view images or a video. However, it is more challenging to reconstruct severely deformed objects from a single-view RGB image in an…

Computer Vision and Pattern Recognition · Computer Science 2022-01-25 Jie Mei , Jingxi Yu , Suzanne Romain , Craig Rose , Kelsey Magrane , Graeme LeeSon , Jenq-Neng Hwang

Existing optimization-based methods for non-rigid registration typically minimize an alignment error metric based on the point-to-point or point-to-plane distance between corresponding point pairs on the source surface and target surface.…

Computer Vision and Pattern Recognition · Computer Science 2025-10-01 Yuxin Yao , Bailin Deng , Junhui Hou , Juyong Zhang

Object recognition has seen significant progress in the image domain, with focus primarily on 2D perception. We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an…

Computer Vision and Pattern Recognition · Computer Science 2020-07-28 Weicheng Kuo , Anelia Angelova , Tsung-Yi Lin , Angela Dai

Incremental Structure from Motion (ISfM) has been widely used for UAV image orientation. Its efficiency, however, decreases dramatically due to the sequential constraint. Although the divide-and-conquer strategy has been utilized for…

Computer Vision and Pattern Recognition · Computer Science 2023-02-08 San Jiang , Qingquan Li , Wanshou Jiang , Wu Chen

Generic 3D reconstruction from a single image is a difficult problem. A lot of data loss occurs in the projection. A domain based approach to reconstruction where we solve a smaller set of problems for a particular use case lead to greater…

Computer Vision and Pattern Recognition · Computer Science 2016-06-21 Atishay Jain