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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 present Large Inverse Rendering Model (LIRM), a transformer architecture that jointly reconstructs high-quality shape, materials, and radiance fields with view-dependent effects in less than a second. Our model builds upon the recent…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Zhengqin Li , Dilin Wang , Ka Chen , Zhaoyang Lv , Thu Nguyen-Phuoc , Milim Lee , Jia-Bin Huang , Lei Xiao , Cheng Zhang , Yufeng Zhu , Carl S. Marshall , Yufeng Ren , Richard Newcombe , Zhao Dong

Neural implicit representation has attracted attention in 3D reconstruction through various success cases. For further applications such as scene understanding or editing, several works have shown progress towards object compositional…

Computer Vision and Pattern Recognition · Computer Science 2024-06-13 Taekbeom Lee , Youngseok Jang , H. Jin Kim

Partial-view 3D recognition -- reconstructing 3D geometry and identifying object instances from a few sparse RGB images -- is an exceptionally challenging yet practically essential task, particularly in cluttered, occluded real-world…

Robotics · Computer Science 2025-07-09 Young Hun Kim , Seungyeon Kim , Yonghyeon Lee , Frank Chongwoo Park

In many inertial confinement fusion experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects which may be…

Computer Vision and Pattern Recognition · Computer Science 2023-02-22 Bradley T. Wolfe , Michael J. Falato , Xinhua Zhang , Nga T. T. Nguyen-Fotiadis , J. P. Sauppe , P. M. Kozlowski , P. A. Keiter , R. E. Reinovsky , S. A. Batha , Zhehui Wang

Dense 3D reconstruction has many applications in automated driving including automated annotation validation, multimodal data augmentation, providing ground truth annotations for systems lacking LiDAR, as well as enhancing auto-labeling…

Computer Vision and Pattern Recognition · Computer Science 2024-02-13 Shihao Shen , Louis Kerofsky , Varun Ravi Kumar , Senthil Yogamani

In this paper, we propose a novel end-to-end relightable neural inverse rendering system that achieves high-quality reconstruction of geometry and material properties, thus enabling high-quality relighting. The cornerstone of our method is…

Computer Vision and Pattern Recognition · Computer Science 2024-10-11 Deheng Zhang , Jingyu Wang , Shaofei Wang , Marko Mihajlovic , Sergey Prokudin , Hendrik P. A. Lensch , Siyu Tang

Implicit surface representations such as the signed distance function (SDF) have emerged as a promising approach for image-based surface reconstruction. However, existing optimization methods assume solid surfaces and are therefore unable…

Computer Vision and Pattern Recognition · Computer Science 2024-11-11 Tianhao Wu , Hanxue Liang , Fangcheng Zhong , Gernot Riegler , Shimon Vainer , Jiankang Deng , Cengiz Oztireli

Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Hadi Alzayer , Philipp Henzler , Jonathan T. Barron , Jia-Bin Huang , Pratul P. Srinivasan , Dor Verbin

Manual modeling of material parameters and 3D geometry is a time consuming yet essential task in the gaming and film industries. While recent advances in 3D reconstruction have enabled accurate approximations of scene geometry and…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Philipp Langsteiner , Jan-Niklas Dihlmann , Hendrik P. A. Lensch

This work addresses the problem of recovering complete, simulatable object geometry from reconstructed real-world scenes, enabling physics-based interaction with objects embedded in the scene. While modern multi-view reconstruction methods…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Xin Dong , Weijian Deng , Lihan Zhang , Tianru Dai , Wenfeng Deng , Yansong Tang

We propose a method for 3D object reconstruction and 6D-pose estimation from 2D images that uses knowledge about object shape as the primary key. In the proposed pipeline, recognition and labeling of objects in 2D images deliver 2D segment…

Computer Vision and Pattern Recognition · Computer Science 2022-03-03 Marcell Wolnitza , Osman Kaya , Tomas Kulvicius , Florentin Wörgötter , Babette Dellen

We consider the problem of reconstructing a full 360{\deg} photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed. We thus take an…

Computer Vision and Pattern Recognition · Computer Science 2023-02-24 Luke Melas-Kyriazi , Christian Rupprecht , Iro Laina , Andrea Vedaldi

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work,…

Computer Vision and Pattern Recognition · Computer Science 2015-02-10 Hang Zhang , Kristin Dana , Ko Nishino

Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis…

Computer Vision and Pattern Recognition · Computer Science 2020-03-30 Michael Oechsle , Michael Niemeyer , Lars Mescheder , Thilo Strauss , Andreas Geiger

We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution manner. To this end, we represent both the bidirectional…

Computer Vision and Pattern Recognition · Computer Science 2023-03-17 Mohammed Brahimi , Bjoern Haefner , Tarun Yenamandra , Bastian Goldluecke , Daniel Cremers

In this paper, we propose a novel method for joint recovery of camera pose, object geometry and spatially-varying Bidirectional Reflectance Distribution Function (svBRDF) of 3D scenes that exceed object-scale and hence cannot be captured…

Computer Vision and Pattern Recognition · Computer Science 2023-06-07 Carolin Schmitt , Božidar Antić , Andrei Neculai , Joo Ho Lee , Andreas Geiger

The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e.g., Street View). Given a sequence of posed RGB…

Computer Vision and Pattern Recognition · Computer Science 2021-11-30 Konstantinos Rematas , Andrew Liu , Pratul P. Srinivasan , Jonathan T. Barron , Andrea Tagliasacchi , Thomas Funkhouser , Vittorio Ferrari

In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capture technique in which the user goes around the object,…

Computer Vision and Pattern Recognition · Computer Science 2021-04-14 Daniel Lichy , Jiaye Wu , Soumyadip Sengupta , David W. Jacobs

We present 3D Surface Splatting (3DSS), the first differentiable surface splatting renderer for physically-based inverse rendering from multi-view images. Our central insight is that the surface separation problem at the heart of surface…

Graphics · Computer Science 2026-05-14 Mae Younes , Adnane Boukhayma