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The recent surge in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction. While these methods demonstrate remarkable performance, the specific drivers behind such gains remain less explored, making a systematic…

Computer Vision and Pattern Recognition · Computer Science 2026-05-06 Lucas Yunkyu Lee , Soonho Kim , Youngwook Kim , Sangmin Kim , Jaesik Park

Temporal retiming, the ability to reconstruct and render dynamic scenes at arbitrary timestamps, is crucial for applications such as slow-motion playback, temporal editing, and post-production. However, most existing 4D Gaussian Splatting…

Computer Vision and Pattern Recognition · Computer Science 2026-05-07 Xuezhen Wang , Li Ma , Yulin Shen , Zeyu Wang , Pedro V. Sander

In this paper, we propose a 3D geometry-aware deformable Gaussian Splatting method for dynamic view synthesis. Existing neural radiance fields (NeRF) based solutions learn the deformation in an implicit manner, which cannot incorporate 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Zhicheng Lu , Xiang Guo , Le Hui , Tianrui Chen , Min Yang , Xiao Tang , Feng Zhu , Yuchao Dai

3D Gaussian Splatting (3DGS) has emerged as a transformative method in the field of real-time novel synthesis. Based on 3DGS, recent advancements cope with large-scale scenes via spatial-based partition strategy to reduce video memory and…

Computer Vision and Pattern Recognition · Computer Science 2025-01-06 Tengfei Wang , Xin Wang , Yongmao Hou , Yiwei Xu , Wendi Zhang , Zongqian Zhan

Recent advancements in 2D and 3D generative models have expanded the capabilities of computer vision. However, generating high-quality 4D dynamic content from a single static image remains a significant challenge. Traditional methods have…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Jing Yang , Yufeng Yang

Gaussian Splatting and its dynamic extensions are effective for reconstructing 3D scenes from 2D images when there is significant camera movement to facilitate motion parallax and when scene objects remain relatively static. However, in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Liuyue Xie , Joel Julin , Koichiro Niinuma , Laszlo A. Jeni

Recent advances in 2D/3D generative models enable the generation of dynamic 3D objects from a single-view video. Existing approaches utilize score distillation sampling to form the dynamic scene as dynamic NeRF or dense 3D Gaussians.…

Computer Vision and Pattern Recognition · Computer Science 2024-08-15 Zijie Wu , Chaohui Yu , Yanqin Jiang , Chenjie Cao , Fan Wang , Xiang Bai

The accurate reconstruction of dynamic street scenes is critical for applications in autonomous driving, augmented reality, and virtual reality. Traditional methods relying on dense point clouds and triangular meshes struggle with moving…

Computer Vision and Pattern Recognition · Computer Science 2026-05-05 Peizhen Zheng , Dongjing Jiang , Qingchong Jiao , Redouane EL Bouchtaoui , Flynnwell Jianfei Zhang

In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Yiming Huang , Beilei Cui , Long Bai , Ziqi Guo , Mengya Xu , Mobarakol Islam , Hongliang Ren

In this paper, we propose a RGB-D SLAM system that reconstructs a language-aligned dense feature field while sustaining low-latency tracking and mapping. First, we introduce a Top-K Rendering pipeline, a high-throughput and…

Robotics · Computer Science 2026-02-10 Seongbo Ha , Sibaek Lee , Kyungsu Kang , Joonyeol Choi , Seungjun Tak , Hyeonwoo Yu

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it…

Computer Vision and Pattern Recognition · Computer Science 2025-06-23 Hongbi Zhou , Zhangkai Ni

Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aquatic environments. This paper presents an enhanced Gaussian Splatting-based framework that…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Zhuodong Jiang , Haoran Wang , Guoxi Huang , Brett Seymour , Nantheera Anantrasirichai

3D Gaussian Splatting (3DGS) has emerged as a real-time, differentiable representation for neural scene understanding. However, existing 3DGS-based methods struggle to represent hierarchical 3D semantic structures and capture whole-part…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Jingbin You , Zehao Li , Hao Jiang , Xinzhu Ma , Shuqin Gao , Honglong Zhao , Congcong Zheng , Tianlu Mao , Feng Dai , Yucheng Zhang , Zhaoqi Wang

Reconstructing intricate, ever-changing environments remains a central ambition in computer vision, yet existing solutions often crumble before the complexity of real-world dynamics. We present DynaSplat, an approach that extends Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2025-06-12 Junli Deng , Ping Shi , Qipei Li , Jinyang Guo

Recent advances in 4D scene reconstruction have significantly improved dynamic modeling across various domains. However, existing approaches remain limited under aerial conditions with single-view capture, wide spatial range, and dynamic…

Computer Vision and Pattern Recognition · Computer Science 2026-02-27 Hanyang Liu , Rongjun Qin

Modern vision language pipelines are driven by RGB vision encoders trained on massive image text corpora. While these pipelines have enabled impressive zero-shot capabilities and strong transfer across tasks, they still inherit two…

Computer Vision and Pattern Recognition · Computer Science 2025-12-25 Yasmine Omri , Connor Ding , Tsachy Weissman , Thierry Tambe

The field of novel view synthesis from images has seen rapid advancements with the introduction of Neural Radiance Fields (NeRF) and more recently with 3D Gaussian Splatting. Gaussian Splatting became widely adopted due to its efficiency…

Computer Vision and Pattern Recognition · Computer Science 2024-11-07 Ruihong Yin , Vladimir Yugay , Yue Li , Sezer Karaoglu , Theo Gevers

Accurate 3D reconstruction in degraded imaging conditions remains a key challenge in photogrammetry and neural rendering. In underwater environments, spatially varying visibility caused by scattering, attenuation, and sparse observations…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Zhuodong Jiang , Haoran Wang , Guoxi Huang , Brett Seymour , Nantheera Anantrasirichai

The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction. Existing approaches mainly rely on full-length multi-view videos, while there has been limited exploration of online…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Zhening Liu , Yingdong Hu , Xinjie Zhang , Rui Song , Jiawei Shao , Zehong Lin , Jun Zhang

Recently, several studies have combined Gaussian Splatting to obtain scene representations with language embeddings for open-vocabulary 3D scene understanding. While these methods perform well, they essentially require very dense multi-view…

Computer Vision and Pattern Recognition · Computer Science 2024-12-05 Jun Hu , Zhang Chen , Zhong Li , Yi Xu , Juyong Zhang