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Gaussian Splatting (GS) has emerged as an effective representation for photorealistic rendering, but the underlying geometry, material, and lighting remain entangled, hindering scene editing. Existing GS-based methods struggle to…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Kang Du , Zhihao Liang , Yulin Shen , Zeyu Wang

Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacement tasks as they do not consider the background, leading to…

Computer Vision and Pattern Recognition · Computer Science 2025-08-25 Hanyuan Xiao , Yingshu Chen , Huajian Huang , Haolin Xiong , Jing Yang , Pratusha Prasad , Yajie Zhao

We present GI-GS, a novel inverse rendering framework that leverages 3D Gaussian Splatting (3DGS) and deferred shading to achieve photo-realistic novel view synthesis and relighting. In inverse rendering, accurately modeling the shading…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Hongze Chen , Zehong Lin , Jun Zhang

We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photorealistic relighting under novel lighting conditions. In physically-based relighting,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-16 Iris Zheng , Guojun Tang , Alexander Doronin , Paul Teal , Fang-Lue Zhang

3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization.However, the original 3D Gaussian Splatting lacks color representation for inputs in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Haoran Wang , Jingwei Huang , Lu Yang , Tianchen Deng , Gaojing Zhang , Mingrui Li

3D Gaussian Splatting (3DGS) has garnered significant attention in robotics for its explicit, high fidelity dense scene representation, demonstrating strong potential for robotic applications. However, 3DGS-based methods in robotics…

Robotics · Computer Science 2025-03-25 Bin Fu , Jialin Li , Bin Zhang , Ruiping Wang , Xilin Chen

We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex appearance, we employ a…

Computer Vision and Pattern Recognition · Computer Science 2024-10-16 Zoubin Bi , Yixin Zeng , Chong Zeng , Fan Pei , Xiang Feng , Kun Zhou , Hongzhi Wu

Recent 3D Gaussian Splatting (3DGS) methods have demonstrated the feasibility of self-driving scene reconstruction and novel view synthesis. However, most existing methods either rely solely on cameras or use LiDAR only for Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 ZY Chen , F Zhu , H Zhu , DY Kong , XK Kuang , YJ Zhang , CM Jiang

3D Gaussian Splatting (3DGS) has become a standard approach to reconstruct and render photorealistic 3D head avatars. A major challenge is to relight the avatars to match any scene illumination. For high quality relighting, existing methods…

Computer Vision and Pattern Recognition · Computer Science 2026-01-08 Yingyan Xu , Pramod Rao , Sebastian Weiss , Gaspard Zoss , Markus Gross , Christian Theobalt , Marc Habermann , Derek Bradley

Relighting a person from a single photo is an attractive but ill-posed task, as a 2D image ambiguously entangles 3D geometry, intrinsic appearance, and illumination. Current methods either use sequential pipelines that suffer from error…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Yuxuan Xue , Ruofan Liang , Egor Zakharov , Timur Bagautdinov , Chen Cao , Giljoo Nam , Shunsuke Saito , Gerard Pons-Moll , Javier Romero

We propose GRGS, a generalizable and relightable 3D Gaussian framework for high-fidelity human novel view synthesis under diverse lighting conditions. Unlike existing methods that rely on per-character optimization or ignore physical…

Computer Vision and Pattern Recognition · Computer Science 2025-11-24 Yipengjing Sun , Shengping Zhang , Chenyang Wang , Shunyuan Zheng , Zonglin Li , Xiangyang Ji

Recent advancements in multi-modal 3D pre-training methods have shown promising efficacy in learning joint representations of text, images, and point clouds. However, adopting point clouds as 3D representation fails to fully capture the…

Computer Vision and Pattern Recognition · Computer Science 2025-02-28 Haoyuan Li , Yanpeng Zhou , Tao Tang , Jifei Song , Yihan Zeng , Michael Kampffmeyer , Hang Xu , Xiaodan Liang

3D Shape represented as point cloud has achieve advancements in multimodal pre-training to align image and language descriptions, which is curial to object identification, classification, and retrieval. However, the discrete representations…

Computer Vision and Pattern Recognition · Computer Science 2024-02-14 Haoyuan Li , Yanpeng Zhou , Yihan Zeng , Hang Xu , Xiaodan Liang

As multimodal language models advance, their application to 3D scene understanding is a fast-growing frontier, driving the development of 3D Vision-Language Models (VLMs). Current methods show strong dependence on object detectors,…

Computer Vision and Pattern Recognition · Computer Science 2025-07-02 Anna-Maria Halacheva , Jan-Nico Zaech , Xi Wang , Danda Pani Paudel , Luc Van Gool

We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results. Unlike previous works that use…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Zhihao Liang , Qi Zhang , Ying Feng , Ying Shan , Kui Jia

We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view. Prior works suffer from inconsistent 3D geometry or mediocre rendering quality due to improper…

Computer Vision and Pattern Recognition · Computer Science 2024-10-30 Yuxuan Mu , Xinxin Zuo , Chuan Guo , Yilin Wang , Juwei Lu , Xiaofeng Wu , Songcen Xu , Peng Dai , Youliang Yan , Li Cheng

Virtual production (VP) use LED walls to provide both background imagery and image-based lighting. While this enables on-set compositing, it couples lighting to background and scene appearance, limiting flexibility for downstream editing.…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Adrian Azzarelli , Nantheera Anantrasirichai , James Pollock , David R. Bull

We present a lighting-aware image editing pipeline that, given a portrait image and a text prompt, performs single image relighting. Our model modifies the lighting and color of both the foreground and background to align with the provided…

Computer Vision and Pattern Recognition · Computer Science 2024-12-19 Junuk Cha , Mengwei Ren , Krishna Kumar Singh , He Zhang , Yannick Hold-Geoffroy , Seunghyun Yoon , HyunJoon Jung , Jae Shin Yoon , Seungryul Baek

Reconstructing 3D assets from images, known as inverse rendering (IR), remains a challenging task due to its ill-posed nature. 3D Gaussian Splatting (3DGS) has demonstrated impressive capabilities for novel view synthesis (NVS) tasks.…

Computer Vision and Pattern Recognition · Computer Science 2025-04-10 Hanxiao Sun , YuPeng Gao , Jin Xie , Jian Yang , Beibei Wang

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
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