The paper presents a lightweight extension to the Monst3R 4D reconstruction model by adding an event-camera input branch while keeping the rest of the architecture unchanged. Across three benchmark datasets, this RGB‑event fusion reduces absolute relative error by an average of 3.4% compared to the RGB‑only baseline, with the largest gains occurring under high object motion where RGB‑only methods falter. The study demonstrates that incorporating event data can meaningfully improve 4D reconstruction robustness without a full architectural redesign.
By Dulanga Weerakoon, Darshana Rathnayake, Van Minh Do, Hemanth Sabbella, Kaichen Zhou, Fangneng Zhan, Alok Prakash, Sanjay E. Sarma, Archan Misra, Paul Pu Liang
RoGe is a new end‑to‑end framework for novel view synthesis that jointly learns an implicit 3D scene representation and a video diffusion model. It eliminates the need for explicit 3D intermediates by querying the implicit scene with camera rays to produce geometric features that condition the diffusion model. Experiments on DL3DV show that RoGe surpasses reconstruction‑based, generation‑based, and hybrid baselines in image quality and temporal consistency, and ablations confirm the benefits of ray‑queried features and joint training.
By Xiaolei Lang, Ze Kang, Zehao Huang, Naiyan Wang
arXiv:2505. 08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes.
By Chuanzhi Xu, Haoxian Zhou, Langyi Chen, Haodong Chen, Zeke Zexi Hu, Zhicheng Lu, Ying Zhou, Vera Chung, Qiang Qu, Weidong Cai
Free-viewpoint 3D scene media is increasingly important for immersive applications, yet practical capture often suffers from severe view sparsity and motion blur. Although neural rendering has advanced sparse-view synthesis, existing blur-aware methods typically require substantial multi-view redundancy, accurate camera poses, or costly per-scene optimization.
GaussVid introduces a 3D-aware video restoration framework that enhances sparse-view 3D Gaussian Splatting (3DGS) reconstructions. By creating a large-scale 3DGS video dataset and employing a camera-conditioned geometric prior anchored on the first and last frames, the method injects spatial structure into video generation, ensuring geometrically grounded restoration across viewpoints. Experiments demonstrate superior pixel- and structure-level fidelity (PSNR/SSIM) and improved multi-view consistency compared to other video-prior restoration methods, while maintaining competitive perceptual quality (LPIPS).
By Xinhui Liu, Can Wang, Wei Jiang, Wei Wang, Dong Xu
arXiv:2608.23175v1 Announce Type: new
Abstract: In light-field (LF) imaging systems, dense spatial sampling from a camera array enables powerful post-capture capabilities such as refocusing and depth...
By Sakshi Goel, Ayush Goyal, K S Venkatesh, Koteswar Rao Jerripothula
arXiv:2607. 01202v1 Announce Type: cross Abstract: We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos.
By Liyuan Zhu, Shengyu Huang, Amrita Mazumdar, Tianye Li, Zan Gojcic, Gordon Wetzstein, Iro Armeni, Shalini De Mello, Alex Trevithick
arXiv:2611.07081v1 Announce Type: new
Abstract: Multi-perspective-camera systems are widely adopted since they provide broad visual coverage for autonomous driving, motivating the synthesis of a unif...
By Kaixuan Zhang, Zhipeng Xiong, Minxian Li, Mingwu Ren, Xiatian Zhu
In light-field (LF) imaging systems, dense spatial sampling from a camera array enables powerful post-capture capabilities such as refocusing and depth estimation. However, real-world LF capture is of...
arXiv:2610.32800v1 Announce Type: new
Abstract: Real-world 3D reconstruction pipelines often rely on multi-view images degraded by lossy compression. While compression artifacts have been extensively...
By Dong-hwi Kim, Chaewon Moon, Hojun Song, Dongbeom Kim, Junyeong Jang, Aro Kim, Gahyeon Kim, Heejung Choi, Jehee Kim, Gianella Cravioto, Sohyun Lee, Gyeongjin Choi, EunHye Jeong, Soo Ye Kim, Jaehyup Lee, Sang-hyo Park
arXiv:2312. 00206v4 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis.
By Haolin Xiong, Sairisheek Muttukuru, Hanyuan Xiao, Rishi Upadhyay, Pradyumna Chari, Yajie Zhao, Achuta Kadambi
GS‑Net is a lightweight plug‑and‑play module that expands sparse Structure‑from‑Motion point clouds into dense Gaussian primitives, enabling cross‑sensor view synthesis for autonomous driving. It learns a generalizable initialization for 3D Gaussian Splatting, improving rendering quality for both interpolated and extrapolated camera viewpoints. The authors introduce CARLA‑NVS, a benchmark with 12 uniformly spaced cameras, and show that GS‑Net outperforms standard 3DGS by 2.08 dB PSNR on interpolated views and 1.86 dB on extrapolated views while being 50× faster to initialize.
By Yichen Zhang, Zihan Wang, Jiali Han, Peilin Li, Jiaxun Zhang, Jianqiang Wang, Lei He, Keqiang Li