Computational neuromorphic imaging (CNI) with event cameras offers advantages such as minimal motion blur and enhanced dynamic range, compared to conventional frame-based methods. Existing event-based radiance field rendering methods are built on neural radiance field, which is computationally heavy and slow in reconstruction speed. Motivated by the two aspects, we introduce Ev-GS, the first CNI-informed scheme to infer 3D Gaussian splatting from a monocular event camera, enabling efficient novel view synthesis. Leveraging 3D Gaussians with pure event-based supervision, Ev-GS overcomes challenges such as the detection of fast-moving objects and insufficient lighting. Experimental results show that Ev-GS outperforms the method that takes frame-based signals as input by rendering realistic views with reduced blurring and improved visual quality. Moreover, it demonstrates competitive reconstruction quality and reduced computing occupancy compared to existing methods, which paves the way to a highly efficient CNI approach for signal processing.
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）
We present neural radiance fields for rendering and temporal (4D) reconstruction of humans in motion (H-NeRF), as captured by a sparse set of cameras or even from a monocular video. Our approach combines ideas from neural scene representation, novel-view synthesis, and implicit statistical geometric human representations, coupled using novel loss functions. Instead of learning a radiance field with a uniform occupancy prior, we constrain it by a structured implicit human body model, represented using signed distance functions. This allows us to robustly fuse information from sparse views and g…
The Chinese University of Hong Kong（香港中文大学）；SmartMore（思谋科技）
Synthesizing photo-realistic images from a point cloud is challenging because of the sparsity of point cloud representation. Recent Neural Radiance Fields and extensions are proposed to synthesize realistic images from 2D input. In this paper, we present Point2Pix as a novel point renderer to link the 3D sparse point clouds with 2D dense image pixels. Taking advantage of the point cloud 3D prior and NeRF rendering pipeline, our method can synthesize high-quality images from colored point clouds, generally for novel indoor scenes. To improve the efficiency of ray sampling, we propose point-guid…
Shanghai Jiao Tong University（上海交通大学）；Anhui University（安徽大学）；Shanghai AI Lab（上海人工智能实验室）
Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF repres…
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）
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）；Google（谷歌）；The Chinese University of Hong Kong（香港中文大学）
Implicit neural rendering techniques have shown promising results for novel view synthesis. However, existing methods usually encode the entire scene as a whole, which is generally not aware of the object identity and limits the ability to the high-level editing tasks such as moving or adding furniture. In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene. Specifically, we design a novel two-pathway architecture, in which the scene…
With the introduction of Neural Radiance Fields (NeRFs), novel view synthesis has recently made a big leap forward. At the core, NeRF proposes that each 3D point can emit radiance, allowing to conduct view synthesis using differentiable volumetric rendering. While neural radiance fields can accurately represent 3D scenes for computing the image rendering, 3D meshes are still the main scene representation supported by most computer graphics and simulation pipelines, enabling tasks such as real time rendering and physics-based simulations. Obtaining 3D meshes from neural radiance fields still re…
GS-IR: 3D Gaussian Splatting for Inverse Rendering
South China University of Technology（华南理工大学）；Tencent AI Lab（腾讯人工智能实验室）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）
LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering
Google（谷歌）；Google DeepMind（谷歌DeepMind）；Technical University of Munich（慕尼黑技术大学）；Czech Technical University in Prague（布拉格捷克技术大学）；Czech Institute of Informatics, Robotics and Cybernetics（捷克信息学、机器人学与自动化研究所）
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）
