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Recent advances in novel-view synthesis can create the photo-realistic visualization of real-world environments from conventional camera captures. However, the everyday environment experiences frequent scene changes, which require dense…

Computer Vision and Pattern Recognition · Computer Science 2026-05-07 Minkwan Kim , Seungmin Lee , Junho Kim , Young Min Kim

We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while…

Computer Vision and Pattern Recognition · Computer Science 2022-12-02 Jing Yu Koh , Harsh Agrawal , Dhruv Batra , Richard Tucker , Austin Waters , Honglak Lee , Yinfei Yang , Jason Baldridge , Peter Anderson

Novel view synthesis with neural models has advanced rapidly in recent years, yet adapting these models to scene changes remains an open problem. Existing methods are either labor-intensive, requiring extensive model retraining, or fail to…

Computer Vision and Pattern Recognition · Computer Science 2025-08-13 Lin Zeng , Boming Zhao , Jiarui Hu , Xujie Shen , Ziqiang Dang , Hujun Bao , Zhaopeng Cui

High dynamic range novel view synthesis (HDR-NVS) reconstructs scenes with dynamic details by fusing multi-exposure low dynamic range (LDR) views, yet it struggles to capture ambient illumination-dependent appearance. Implicitly supervising…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Huimin Zeng , Yue Bai , Hailing Wang , Yun Fu

The field of Novel View Synthesis has been revolutionized by 3D Gaussian Splatting (3DGS), which enables high-quality scene reconstruction that can be rendered in real-time. 3DGS-based techniques typically suffer from high GPU memory and…

Graphics · Computer Science 2025-10-27 Umar Farooq , Jean-Yves Guillemaut , Adrian Hilton , Marco Volino

Neural rendering has demonstrated remarkable success in high-quality 3D neural reconstruction and novel view synthesis with dense input views and accurate poses. However, applying it to extremely sparse, unposed views in unbounded 360{\deg}…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Chong Bao , Xiyu Zhang , Zehao Yu , Jiale Shi , Guofeng Zhang , Songyou Peng , Zhaopeng Cui

3D scene reconstruction is essential for applications in virtual reality, robotics, and autonomous driving, enabling machines to understand and interact with complex environments. Traditional 3D Gaussian Splatting techniques rely on images…

Graphics · Computer Science 2025-03-04 Changlin Song , Jiaqi Wang , Liyun Zhu , He Weng

Novel view synthesis from a single image has recently attracted a lot of attention, and it has been primarily advanced by 3D deep learning and rendering techniques. However, most work is still limited by synthesizing new views within…

Computer Vision and Pattern Recognition · Computer Science 2022-03-18 Xuanchi Ren , Xiaolong Wang

Dynamic scene reconstruction from monocular video is essential for real-world applications. We introduce DGNS, a hybrid framework integrating \underline{D}eformable \underline{G}aussian Splatting and Dynamic \underline{N}eural…

Computer Vision and Pattern Recognition · Computer Science 2025-08-15 Xuesong Li , Jinguang Tong , Jie Hong , Vivien Rolland , Lars Petersson

We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes. Previous methods extending 3DGS to model dynamic…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Cheng-De Fan , Chen-Wei Chang , Yi-Ruei Liu , Jie-Ying Lee , Jiun-Long Huang , Yu-Chee Tseng , Yu-Lun Liu

The reconstruction of dynamic 3D scenes using 3D Gaussian Splatting has shown significant promise. A key challenge, however, remains in modeling realistic motion, as most methods fail to align the motion of Gaussians with real-world…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Junoh Lee , Junmyeong Lee , Yeon-Ji Song , Inhwan Bae , Jisu Shin , Hae-Gon Jeon , Jin-Hwa Kim

3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D scene representation. Despite their impressive performance, they confront challenges due to the limitation of structure-from-motion (SfM) methods on acquiring accurate…

Computer Vision and Pattern Recognition · Computer Science 2025-01-28 Ao Gao , Luosong Guo , Tao Chen , Zhao Wang , Ying Tai , Jian Yang , Zhenyu Zhang

Stylizing a dynamic scene based on an exemplar image is critical for various real-world applications, including gaming, filmmaking, and augmented and virtual reality. However, achieving consistent stylization across both spatial and…

Computer Vision and Pattern Recognition · Computer Science 2025-01-08 Abhishek Saroha , Florian Hofherr , Mariia Gladkova , Cecilia Curreli , Or Litany , Daniel Cremers

3D Gaussian Splatting (3DGS) has recently gained popularity for efficient scene rendering by representing scenes as explicit sets of anisotropic 3D Gaussians. However, most existing work focuses primarily on modeling external surfaces. In…

Image and Video Processing · Electrical Eng. & Systems 2026-01-12 Shuxin Liang , Yihan Xiao , Wenlu Tang

3D Gaussian splatting has surpassed neural radiance field methods in novel view synthesis by achieving lower computational costs and real-time high-quality rendering. Although it produces a high-quality rendering with a lot of input views,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-16 Raja Kumar , Vanshika Vats

Accurately and efficiently modeling dynamic scenes and motions is considered so challenging a task due to temporal dynamics and motion complexity. To address these challenges, we propose DynMF, a compact and efficient representation that…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Agelos Kratimenos , Jiahui Lei , Kostas Daniilidis

We introduce a fully automatic pipeline for dynamic scene reconstruction from casually captured monocular RGB videos. Rather than designing a new scene representation, we enhance the priors that drive Dynamic Gaussian Splatting. Video…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Meng-Li Shih , Ying-Huan Chen , Yu-Lun Liu , Brian Curless

3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation enabling real-time, high-fidelity 3D reconstruction and novel view synthesis. However, its practical use is hindered by the massive memory and computational…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Seokhyun Youn , Soohyun Lee , Geonho Kim , Weeyoung Kwon , Sung-Ho Bae , Jihyong Oh

Representing and rendering dynamic scenes has been an important but challenging task. Especially, to accurately model complex motions, high efficiency is usually hard to guarantee. To achieve real-time dynamic scene rendering while also…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Guanjun Wu , Taoran Yi , Jiemin Fang , Lingxi Xie , Xiaopeng Zhang , Wei Wei , Wenyu Liu , Qi Tian , Xinggang Wang

Reconstructing high-quality 3D models from sparse 2D images has garnered significant attention in computer vision. Recently, 3D Gaussian Splatting (3DGS) has gained prominence due to its explicit representation with efficient training speed…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Keng-Wei Chang , Zi-Ming Wang , Shang-Hong Lai