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Related papers: DecoRec: Decomposed 3D Scene Reconstruction from S…

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3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However, recovering a high-quality NeRF typically requires tens to hundreds of input images, resulting in a…

Computer Vision and Pattern Recognition · Computer Science 2023-12-06 Rundi Wu , Ben Mildenhall , Philipp Henzler , Keunhong Park , Ruiqi Gao , Daniel Watson , Pratul P. Srinivasan , Dor Verbin , Jonathan T. Barron , Ben Poole , Aleksander Holynski

We present a method for relighting 3D reconstructions of large room-scale environments. Existing solutions for 3D scene relighting often require solving under-determined or ill-conditioned inverse rendering problems, and are as such unable…

Computer Vision and Pattern Recognition · Computer Science 2026-01-30 Xiaoyan Xing , Philipp Henzler , Junhwa Hur , Runze Li , Jonathan T. Barron , Pratul P. Srinivasan , Dor Verbin

Existing multi-view 3D object reconstruction methods heavily rely on sufficient overlap between input views, where occlusions and sparse coverage in practice frequently yield severe reconstruction incompleteness. Recent advancements in…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Jiahao Chang , Chongjie Ye , Yushuang Wu , Yuantao Chen , Yidan Zhang , Zhongjin Luo , Chenghong Li , Yihao Zhi , Xiaoguang Han

The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have…

Computer Vision and Pattern Recognition · Computer Science 2024-08-20 Ruofan Liang , Zan Gojcic , Merlin Nimier-David , David Acuna , Nandita Vijaykumar , Sanja Fidler , Zian Wang

Reconstructing physically stable 3D scenes from a single RGB image enables casual images to be converted into simulation-ready digital assets for applications such as immersive interaction and content creation. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Xiaoxuan Ma , Jiashun Wang , Nicolas Ugrinovic , Yehonathan Litman , Kris Kitani

We present Viewset Diffusion, a diffusion-based generator that outputs 3D objects while only using multi-view 2D data for supervision. We note that there exists a one-to-one mapping between viewsets, i.e., collections of several 2D views of…

Computer Vision and Pattern Recognition · Computer Science 2023-09-04 Stanislaw Szymanowicz , Christian Rupprecht , Andrea Vedaldi

Humans can naturally identify and mentally complete occluded objects in cluttered environments. However, imparting similar cognitive ability to robotics remains challenging even with advanced reconstruction techniques, which models scenes…

Computer Vision and Pattern Recognition · Computer Science 2025-07-22 Zesong Yang , Bangbang Yang , Wenqi Dong , Chenxuan Cao , Liyuan Cui , Yuewen Ma , Zhaopeng Cui , Hujun Bao

Enabling generative models to decompose visual concepts from a single image is a complex and challenging problem. In this paper, we study a new and challenging task, customized concept decomposition, wherein the objective is to leverage…

Computer Vision and Pattern Recognition · Computer Science 2024-10-02 Zhi Xu , Shaozhe Hao , Kai Han

Neural Radiance Fields and 3D Gaussian Splatting have revolutionized 3D reconstruction and novel-view synthesis task. However, achieving photorealistic rendering from extreme novel viewpoints remains challenging, as artifacts persist across…

Computer Vision and Pattern Recognition · Computer Science 2025-03-04 Jay Zhangjie Wu , Yuxuan Zhang , Haithem Turki , Xuanchi Ren , Jun Gao , Mike Zheng Shou , Sanja Fidler , Zan Gojcic , Huan Ling

We introduce ReStyle3D, a novel framework for scene-level appearance transfer from a single style image to a real-world scene represented by multiple views. The method combines explicit semantic correspondences with multi-view consistency…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Liyuan Zhu , Shengqu Cai , Shengyu Huang , Gordon Wetzstein , Naji Khosravan , Iro Armeni

This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images. Designed to work with as few as one view, 3DFIRES reconstructs the complete geometry of unseen scenes, including hidden surfaces. With…

Computer Vision and Pattern Recognition · Computer Science 2024-03-14 Linyi Jin , Nilesh Kulkarni , David Fouhey

We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but…

Computer Vision and Pattern Recognition · Computer Science 2023-02-17 Zhizhuo Zhou , Shubham Tulsiani

Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects. This work presents DisCoScene: a 3Daware generative…

Computer Vision and Pattern Recognition · Computer Science 2022-12-23 Yinghao Xu , Menglei Chai , Zifan Shi , Sida Peng , Ivan Skorokhodov , Aliaksandr Siarohin , Ceyuan Yang , Yujun Shen , Hsin-Ying Lee , Bolei Zhou , Sergey Tulyakov

Unsupervised learning of object-centric representations in dynamic visual scenes is challenging. Unlike most previous approaches that learn to decompose 2D images, we present DynaVol, a 3D scene generative model that unifies geometric…

Computer Vision and Pattern Recognition · Computer Science 2024-01-29 Yanpeng Zhao , Siyu Gao , Yunbo Wang , Xiaokang Yang

Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward model to reconstruct images to 3D content.However,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Zhenyu Tang , Junwu Zhang , Xinhua Cheng , Wangbo Yu , Chaoran Feng , Yatian Pang , Bin Lin , Li Yuan

We introduce PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed distance fields and post-hoc layout optimization, PixARMesh…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Xiang Zhang , Sohyun Yoo , Hongrui Wu , Chuan Li , Jianwen Xie , Zhuowen Tu

Single-view 3D scene reconstruction involves inferring both object geometry and spatial layout. Existing methods typically reconstruct objects independently or rely on implicit scene context, failing to exploit the repeated instances…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Paul Dobre , Xin Wang , Hongzhou Yang

Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Haibo Yang , Yang Chen , Yingwei Pan , Ting Yao , Zhineng Chen , Chong-Wah Ngo , Tao Mei

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to…

Computer Vision and Pattern Recognition · Computer Science 2024-02-02 Weixing Xie , Xiao Dong , Yong Yang , Qiqin Lin , Jingze Chen , Junfeng Yao , Xiaohu Guo

In this paper, we present PanoDreamer, a novel method for producing a coherent 360{\deg} 3D scene from a single input image. Unlike existing methods that generate the scene sequentially, we frame the problem as single-image panorama and…

Computer Vision and Pattern Recognition · Computer Science 2026-01-22 Avinash Paliwal , Xilong Zhou , Andrii Tsarov , Nima Khademi Kalantari