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3D reconstruction methods such as 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) achieve impressive photorealism but fail when input images suffer from severe motion blur. While event cameras provide high-temporal-resolution…

Computer Vision and Pattern Recognition · Computer Science 2026-05-11 Jun Dai , Renbiao Jin , Bo Xu , Yutian Chen , Linning Xu , Mulin Yu , Tianfan Xue , Shi Guo

When viewing a 3D Gaussian Splatting (3DGS) model from camera positions significantly outside the training data distribution, substantial visual noise commonly occurs. These artifacts result from the lack of training data in these…

Computer Vision and Pattern Recognition · Computer Science 2025-10-24 Damian Bowness , Charalambos Poullis

Reconstructing dynamic 3D scenes from monocular videos is a fundamental yet highly challenging task, as real-world motions often involve both long-term smooth transformations and short-term complex deformations. Existing methods either…

Computer Vision and Pattern Recognition · Computer Science 2026-05-25 Chenyu Wu , Wanhua Li , Zhu-Tian Chen , Hanspeter Pfister

Rendering dynamic scenes from monocular videos is a crucial yet challenging task. The recent deformable Gaussian Splatting has emerged as a robust solution to represent real-world dynamic scenes. However, it often leads to heavily redundant…

Computer Vision and Pattern Recognition · Computer Science 2025-02-28 Hanyang Kong , Xingyi Yang , Xinchao Wang

Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity…

Computer Vision and Pattern Recognition · Computer Science 2020-01-14 Anton Mitrokhin , Cornelia Fermuller , Chethan Parameshwara , Yiannis Aloimonos

Modeling Neural Radiance Fields for fast-moving deformable objects from visual data alone is a challenging problem. A major issue arises due to the high deformation and low acquisition rates. To address this problem, we propose to use event…

Computer Vision and Pattern Recognition · Computer Science 2023-09-26 Qi Ma , Danda Pani Paudel , Ajad Chhatkuli , Luc Van Gool

Gaussian Splatting (GS) has significantly elevated scene reconstruction efficiency and novel view synthesis (NVS) accuracy compared to Neural Radiance Fields (NeRF), particularly for dynamic scenes. However, current 4D NVS methods, whether…

Computer Vision and Pattern Recognition · Computer Science 2025-04-02 Fang Li , Hao Zhang , Narendra Ahuja

Capturing general deforming scenes from monocular RGB video is crucial for many computer graphics and vision applications. However, current approaches suffer from drawbacks such as struggling with large scene deformations, inaccurate shape…

Computer Vision and Pattern Recognition · Computer Science 2023-05-05 Erik C. M. Johnson , Marc Habermann , Soshi Shimada , Vladislav Golyanik , Christian Theobalt

Reconstructing dynamic humans together with static scenes from monocular videos remains difficult, especially under fast motion, where RGB frames suffer from motion blur. Event cameras exhibit distinct advantages, e.g., microsecond temporal…

Computer Vision and Pattern Recognition · Computer Science 2025-09-24 Xiaoting Yin , Hao Shi , Kailun Yang , Jiajun Zhai , Shangwei Guo , Lin Wang , Kaiwei Wang

Novel view synthesis (NVS) through non-planar refractive surfaces presents fundamental challenges due to severe, spatially varying optical distortions. While recent representations like NeRF and 3D Gaussian Splatting (3DGS) excel at NVS,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Yiming Shao , Qiyu Dai , Chong Gao , Guanbin Li , Yeqiang Wang , He Sun , Qiong Zeng , Baoquan Chen , Wenzheng Chen

We present EvDNeRF, a pipeline for generating event data and training an event-based dynamic NeRF, for the purpose of faithfully reconstructing eventstreams on scenes with rigid and non-rigid deformations that may be too fast to capture…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Anish Bhattacharya , Ratnesh Madaan , Fernando Cladera , Sai Vemprala , Rogerio Bonatti , Kostas Daniilidis , Ashish Kapoor , Vijay Kumar , Nikolai Matni , Jayesh K. Gupta

In this paper, we propose MoDGS, a new pipeline to render novel views of dy namic scenes from a casually captured monocular video. Previous monocular dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid move ment of input…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Qingming Liu , Yuan Liu , Jiepeng Wang , Xianqiang Lyv , Peng Wang , Wenping Wang , Junhui Hou

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

While 3D Gaussian Splatting (3D-GS) achieves photorealistic novel view synthesis, its performance degrades with motion blur. In scenarios with rapid motion or low-light conditions, existing RGB-based deblurring methods struggle to model…

Computer Vision and Pattern Recognition · Computer Science 2025-07-14 Yufei Deng , Yuanjian Wang , Rong Xiao , Chenwei Tang , Jizhe Zhou , Jiahao Fan , Deng Xiong , Jiancheng Lv , Huajin Tang

Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes featuring transient objects is challenging. We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per…

Computer Vision and Pattern Recognition · Computer Science 2025-03-03 Jingyu Lin , Jiaqi Gu , Lubin Fan , Bojian Wu , Yujing Lou , Renjie Chen , Ligang Liu , Jieping Ye

Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed and efficiency in vision sensing. Despite their high dynamic range, temporal resolution, low power…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Seyed Ehsan Marjani Bajestani , Giovanni Beltrame

4D reconstruction from casually captured monocular videos is challenging due to inherent ambiguity in reconstructing dynamic 3D geometry. To address this challenge, we introduce Robust Dynamic Gaussian Splatting (RoDyGS), a method that…

Computer Vision and Pattern Recognition · Computer Science 2026-05-07 Junmyeong Lee , Hoseung Choi , Yoonwoo Jeong , Minsu Cho

Novel View Synthesis (NVS) from unconstrained photo collections is challenging in computer graphics. Recently, 3D Gaussian Splatting (3DGS) has shown promise for photorealistic and real-time NVS of static scenes. Building on 3DGS, we…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Yuze Wang , Junyi Wang , Yue Qi

Event cameras offer a considerable alternative to RGB cameras in many scenarios. While there are recent works on event-based novel-view synthesis, dense 3D mesh reconstruction remains scarcely explored and existing event-based techniques…

Computer Vision and Pattern Recognition · Computer Science 2026-02-04 Shreyas Sachan , Viktor Rudnev , Mohamed Elgharib , Christian Theobalt , Vladislav Golyanik

Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time is challenging due to scene complexity and temporal dynamics. Despite advancements in neural implicit models, limitations persist: (i) Inadequate Scene…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 Zeyu Yang , Hongye Yang , Zijie Pan , Li Zhang