Dynamic 2D Gaussians: Geometrically Accurate Radiance Fields for Dynamic Objects
School of EIC Huazhong University of Science（电子信息学院华中科技大学）；School of CS Huazhong University of Science（计算机学院华中科技大学）；Huazhong University of Science（华中科技大学）
Shuai Zhang, Guanjun Wu, Zhoufeng Xie, Xinggang Wang, Bin Feng, Wenyu Liu
Reconstructing objects and extracting high-quality surfaces play a vital role in the real world. Current 4D representations show the ability to render high-quality novel views for dynamic objects, but cannot reconstruct high-quality meshes due to their implicit or geometrically inaccurate representations. In this paper, we propose a novel representation that can reconstruct accurate meshes from sparse image input, named Dynamic 2D Gaussians (D-2DGS). We adopt 2D Gaussians for basic geometry representation and use sparse-controlled points to capture the 2D Gaussian's deformation. By extracting the object mask from the rendered high-quality image and masking the rendered depth map, we remove floaters that are prone to occur during reconstruction and can extract high-quality dynamic mesh sequences of dynamic objects. Experiments demonstrate that our D-2DGS is outstanding in reconstructing detailed and smooth high-quality meshes from sparse inputs. The code is available at https://github.com/hustvl/Dynamic-2DGS.
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…
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）
Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camera poses. Although approaches for jointly recovering the radiance field and camera pose exist (BARF), they rely on a cumbersome coarse-to-fine auxiliary positional embedding to ensure good performance. We present Gaussian Activated neural Radiance Fields (GARF), a new positional embedding-free neural radiance field architecture - employing Gaussian activations - that outperforms the current state-of-the-art in terms o…
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…
DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization
Beihang University（北京航空航天大学）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；Griffith University（格里菲斯大学）；RIKEN AIP（理化学研究所人工智能项目）；The University of Tokyo（东京大学）
Google Research（谷歌研究院）；University of Washington（华盛顿大学）
We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images. In contrast to previous work, we are able to do this whilst simultaneously separating foreground objects from their varying backgrounds. We achieve this via a 2-component NeRF model, FiG-NeRF, that prefers explanation of the scene as a geometrically constant background and a deformable foreground that represents the object category. We show that this method can learn accurate 3D object category models using only photometric supervision and casually captured …
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）
Advances in Radiance Field for Dynamic Scene: From Neural Field to Gaussian Field
School of Electronics and Information, Hangzhou Dianzi University（电子信息学院，杭州电子科技大学）；School of Computer Science, Wuhan University（计算机学院，武汉大学）；Melbourne Centre for Data Science, School of Mathematics and Statistics, University of Melbourne（数据科学中心，墨尔本大学）；College of Computing & Data Science at Nanyang Technological University（计算与数据科学学院，南洋理工大学）
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…
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）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carnegie Mellon University（卡内基梅隆大学）
Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception. Most of the existing work has focused on developing data-driven discriminative models for scene understanding. This paper provides a new approach to scene understanding, from a synthesis model perspective, by leveraging the recent progress on implicit 3D representation and neural rendering. Building upon the great success of Neural Radiance Fields (NeRFs), we introduce Scene-Property Synthesis with NeRF (SS-NeRF) that is able to not only render photo-rea…
BEA-GS: BEyond RAdiance Supervision in 3DGS for Precise Object Extraction
Arquimea Research Center（阿奎米亚研究中心）；Institut de Robòtica i Informàtica Industrial (CSIC-UPC)（机器人与信息技术研究所（CSIC-UPC））；Universidad de las Palmas de Gran Canaria (IUSIANI)（Gran Canaria大学（IUSIANI））
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）
Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field
Max-Planck-Institut für Informatik（马克斯·普朗克信息学研究所）；Technische Universität Wien（维也纳技术大学）；Carnegie Mellon University（卡内基梅隆大学）；Universidad de Zaragoza（萨拉戈萨大学）
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）
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）
360-GeoGS: Geometrically Consistent Feed-Forward 3D Gaussian Splatting Reconstruction for 360 Images
Shanghai Key Laboratory of Navigation and Location Based Services（上海导航与基于位置的服务关键实验室）；Shanghai Jiao Tong University（上海交通大学）；State Key Laboratory of Submarine Geoscience（海底地球科学国家重点实验室）；School of Automation and Intelligent Sensing（自动化与智能感知学院）
MixedGaussianAvatar: Realistically and Geometrically Accurate Head Avatar via Mixed 2D-3D Gaussians
University of the Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）；Nankai University（南开大学）；Tsinghua University（清华大学）；Intel Labs China（英特尔中国实验室）
