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Related papers: DiffGS: Functional Gaussian Splatting Diffusion

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Recent advancements in 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have achieved impressive results in real-time 3D reconstruction and novel view synthesis. However, these methods struggle in large-scale, unconstrained…

Computer Vision and Pattern Recognition · Computer Science 2025-04-03 Niluthpol Chowdhury Mithun , Tuan Pham , Qiao Wang , Ben Southall , Kshitij Minhas , Bogdan Matei , Stephan Mandt , Supun Samarasekera , Rakesh Kumar

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time, high-resolution novel view synthesis. By representing scenes as a mixture of Gaussian primitives, 3DGS leverages GPU rasterization pipelines for efficient…

Computer Vision and Pattern Recognition · Computer Science 2025-05-12 Peihao Wang , Yuehao Wang , Dilin Wang , Sreyas Mohan , Zhiwen Fan , Lemeng Wu , Ruisi Cai , Yu-Ying Yeh , Zhangyang Wang , Qiang Liu , Rakesh Ranjan

3D Gaussian Splatting (3DGS) has recently emerged as a fast, high-quality method for novel view synthesis (NVS). However, its use of low-degree spherical harmonics limits its ability to capture spatially varying color and view-dependent…

Computer Vision and Pattern Recognition · Computer Science 2025-11-19 Hoang Chuong Nguyen , Wei Mao , Jose M. Alvarez , Miaomiao Liu

3D open-vocabulary scene understanding, which accurately perceives complex semantic properties of objects in space, has gained significant attention in recent years. In this paper, we propose GAGS, a framework that distills 2D CLIP features…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Yuning Peng , Haiping Wang , Yuan Liu , Chenglu Wen , Zhen Dong , Bisheng Yang

3D Gaussian splatting (3DGS) is an innovative rendering technique that surpasses the neural radiance field (NeRF) in both rendering speed and visual quality by leveraging an explicit 3D scene representation. Existing 3DGS approaches require…

Computer Vision and Pattern Recognition · Computer Science 2025-06-13 Lintao Xiang , Hongpei Zheng , Yating Huang , Qijun Yang , Hujun Yin

3D Gaussian Splatting (3DGS) revolutionized novel view rendering. Instead of inferring from dense spatial points, as implicit representations do, 3DGS uses sparse Gaussians. This enables real-time performance but increases space…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Cem Eteke , Enzo Tartaglione

We introduce NovelGS, a diffusion model for Gaussian Splatting (GS) given sparse-view images. Recent works leverage feed-forward networks to generate pixel-aligned Gaussians, which could be fast rendered. Unfortunately, the method was…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Jinpeng Liu , Jiale Xu , Weihao Cheng , Yiming Gao , Xintao Wang , Ying Shan , Yansong Tang

3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual quality. However, existing methods struggle with semi-transparent specular surfaces that exhibit both complex reflections and clear transmission, often…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Ji Shi , Xianghua Ying , Bowei Xing , Ruohao Guo , Wenzhen Yue

Recently, Gaussian Splatting, a method that represents a 3D scene as a collection of Gaussian distributions, has gained significant attention in addressing the task of novel view synthesis. In this paper, we highlight a fundamental…

Computer Vision and Pattern Recognition · Computer Science 2024-10-31 Haoxuan Qu , Zhuoling Li , Hossein Rahmani , Yujun Cai , Jun Liu

4D content generation has achieved remarkable progress recently. However, existing methods suffer from long optimization times, a lack of motion controllability, and a low quality of details. In this paper, we introduce DreamGaussian4D…

Computer Vision and Pattern Recognition · Computer Science 2024-06-11 Jiawei Ren , Liang Pan , Jiaxiang Tang , Chi Zhang , Ang Cao , Gang Zeng , Ziwei Liu

3D Gaussian Splatting (3DGS) has emerged as a novel explicit representation for 3D scenes, offering both high-fidelity reconstruction and efficient rendering. However, 3DGS lacks 3D segmentation ability, which limits its applicability in…

Computer Vision and Pattern Recognition · Computer Science 2025-08-28 Yupeng Zhang , Dezhi Zheng , Ping Lu , Han Zhang , Lei Wang , Liping xiang , Cheng Luo , Kaijun Deng , Xiaowen Fu , Linlin Shen , Jinbao Wang

The recent success of 3D Gaussian Splatting (3DGS) has reshaped novel view synthesis by enabling fast optimization and real-time rendering of high-quality radiance fields. However, it relies on simplified, order-dependent alpha blending and…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Jan U. Müller , Robin Tim Landsgesell , Leif Van Holland , Patrick Stotko , Reinhard Klein

3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magnitude of view-space positional gradients to grow Gaussians to…

Computer Vision and Pattern Recognition · Computer Science 2024-12-09 Qingshan Xu , Jiequan Cui , Xuanyu Yi , Yuxuan Wang , Yuan Zhou , Yew-Soon Ong , Hanwang Zhang

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

3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Aashish Rai , Dilin Wang , Mihir Jain , Nikolaos Sarafianos , Kefan Chen , Srinath Sridhar , Aayush Prakash

We present DGGS, a novel framework that addresses the previously unexplored challenge: $\textbf{Distractor-free Generalizable 3D Gaussian Splatting}$ (3DGS). It mitigates 3D inconsistency and training instability caused by distractor data…

Computer Vision and Pattern Recognition · Computer Science 2026-02-27 Yanqi Bao , Jing Liao , Jing Huo , Yang Gao

Gaussian splatting typically requires dense observations of the scene and can fail to reconstruct occluded and unobserved areas. We propose a latent diffusion model to reconstruct a complete 3D scene with Gaussian splats, including the…

Computer Vision and Pattern Recognition · Computer Science 2025-09-01 Ziwei Liao , Mohamed Sayed , Steven L. Waslander , Sara Vicente , Daniyar Turmukhambetov , Michael Firman

Real-time rendering of dynamic scenes with view-dependent effects remains a fundamental challenge in computer graphics. While recent advances in Gaussian Splatting have shown promising results separately handling dynamic scenes (4DGS) and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Zhongpai Gao , Benjamin Planche , Meng Zheng , Anwesa Choudhuri , Terrence Chen , Ziyan Wu

Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, while diffusion-based repair is typically restricted to…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Rui Song , Tianhui Cai , Markus Gross , Xingcheng Zhou , Zewei Zhou , Zhiyu Huang , Olaf Wysocki , Jiaqi Ma

Recently, 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians. The predominant optimization method for these models relies on backpropagating gradients through a differentiable…

Computer Vision and Pattern Recognition · Computer Science 2025-09-24 Toon Van de Maele , Ozan Catal , Alexander Tschantz , Christopher L. Buckley , Tim Verbelen