We propose progressive radiance distillation, an inverse rendering method that combines physically-based rendering with Gaussian-based radiance field rendering using a distillation progress map. Taking multi-view images as input, our method starts from a pre-trained radiance field guidance, and distills physically-based light and material parameters from the radiance field using an image-fitting process. The distillation progress map is initialized to a small value, which favors radiance field rendering. During early iterations when fitted light and material parameters are far from convergence, the radiance field fallback ensures the sanity of image loss gradients and avoids local minima that attracts under-fit states. As fitted parameters converge, the physical model gradually takes over and the distillation progress increases correspondingly. In presence of light paths unmodeled by the physical model, the distillation progress never finishes on affected pixels and the learned radiance field stays in the final rendering. With this designed tolerance for physical model limitations, we prevent unmodeled color components from leaking into light and material parameters, alleviating relighting artifacts. Meanwhile, the remaining radiance field compensates for the limitations of the physical model, guaranteeing high-quality novel views synthesis. Experimental results demonstrate that our method significantly outperforms state-of-the-art techniques quality-wise in both novel view synthesis and relighting. The idea of progressive radiance distillation is not limited to Gaussian splatting. We show that it also has positive effects for prominently specular scenes when adapted to a mesh-based inverse rendering method.
Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（信息科学与电子工程学院，上海交通大学）；University of Missouri-Kansas City（密苏里大学-凯斯城分校）；Lenovo Group, Lenovo Research（联想集团，联想研究院）
Triangle Splatting for Real-Time Radiance Field Rendering
University of Liège（列日大学）；KAUST（阿卜杜拉国王科技大学）；University of Oxford（牛津大学）；Simon Fraser University（西蒙弗雷泽大学）；University of Toronto（多伦多大学）；Google DeepMind（谷歌DeepMind）
DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior
Department of Computer Science, Dalian University of Technology（大连理工大学计算机科学系）；Department of Mechano-informatics, Information Science and Technology, The University of Tokyo（东京大学机械信息学系、信息科学与技术系）；Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University（上海交通大学医学机器人研究所、自动化系）；Key Laboratory of System Control and Information Processing, Ministry of Education（教育部系统控制与信息处理重点实验室）
SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep
Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；State Key Laboratory for Multimedia Information Processing and National Engineering Research Center of Visual Technology, School of Computer Science, Peking University（北京大学信息多媒体国家重点实验室和视觉技术国家工程研究中心、计算机科学学院）
Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Guangdong Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）；Adobe Inc.（奥多比公司）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）
Technical University of Munich（慕尼黑工业大学）；Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Google（谷歌）
Simultaneously localizing camera poses and constructing Gaussian radiance fields in dynamic scenes establish a crucial bridge between 2D images and the 4D real world. Instead of removing dynamic objects as distractors and reconstructing only static environments, this paper proposes an efficient architecture that incrementally tracks camera poses and establishes the 4D Gaussian radiance fields in unknown scenarios by using a sequence of RGB-D images. First, by generating motion masks, we obtain static and dynamic priors for each pixel. To eliminate the influence of static scenes and improve the…
PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting
Oregon State University（俄勒冈州立大学）；The Ohio State University（俄亥俄州立大学）；Argonne National Laboratory（阿贡国家实验室）；The University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）
MrGS: Multi-modal Radiance Fields with 3D Gaussian Splatting for RGB-Thermal Novel View Synthesis
Minnesota Robotics Institute (MnRI), University of Minnesota, Twin Cities（明尼苏达大学罗学院（MnRI）、明尼苏达大学双城分校）；Department of Information Convergence Engineering (Artificial Intelligence Major), Pusan National University（信息融合工程系（人工智能专业），釜山国立大学）；Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH)（能源工程系，韩国能源技术研究所（KENTECH））；School of Computer Science and Engineering, Pusan National University（计算机科学与工程学院，釜山国立大学）
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
Nanyang Technological University（南洋理工大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Carnegie Mellon University（卡内基梅隆大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）
Progressively Deformable 2D Gaussian Splatting for Video Representation at Arbitrary Resolutions
Shanghai Jiao Tong University, Shanghai, China（上海交通大学）；University of Missouri--Kansas City, Kansas City, MO, USA（密苏里大学-堪萨斯城分校）；University of Canterbury, Christchurch, New Zealand（坎特伯雷大学）
Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers down…
Triangle Splatting+: Differentiable Rendering with Opaque Triangles
University of Liège（利根大学）；Simon Fraser University（西蒙弗雷泽大学）；University of Maryland（马里兰大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Toronto（多伦多大学）；Adobe Research（Adobe研究）
