3D Reconstruction

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GSurf: Learning Signed Distance Fields from Splatting Opaque Gaussians for High-quality 3D Reconstruction
Nov 24, 2024Baixin Xu, Jiangbei Hu, Jiaze Li +13D Gaussian Splatting Signed Distance Functions
College of Computing and Data Science, Nanyang Technological University · School of Software, Dalian University of Technology
PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing
Sep 16, 2024Peng Li, Wangguandong Zheng, Yuan Liu +93D Reconstruction 3D Mesh Reconstruction
HKUST · Southeast University · Tsinghua University +1
2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Features
Aug 30, 2024Ruihan Yu, Tianyu Huang, Jingwang Ling +13D Gaussian Splatting 3D Reconstruction
Department of Physics, Tsinghua University · School of software and BNRist, Tsinghua University
Multi-view Hand Reconstruction with a Point-Embedded Transformer
Aug 20, 2024Lixin Yang, Licheng Zhong, Pengxiang Zhu +43D ViTs Markerless Motion Capture
School of Artificial Intelligence (SAI), Shanghai Jiao Tong University, Shanghai 200230, China · School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China +1
EgoFun3D: Modeling Interactive Objects from Egocentric Videos using Function Templates
Date pendingWeikun Peng, Denys Iliash, Manolis SavvaArticulated Object Reconstruction 3D Part Segmentation
Simon Fraser University
Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation
Date pendingElton Cao, Hod Lipson3D Reconstruction Depth Estimation
Creative Machines Lab, Columbia University New York, NY
Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning
Date pendingXuyang Wang, Zhenyu Li, Jian Ding +4Articulated Object Reconstruction 3D Reconstruction
Australian National University, Canberra, Australia · King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
NSL-SLAM: High-Fidelity Neural Structured-Light Depth for Practical SLAM and Reconstruction
Date pendingJiaheng Li, Binsheng Zhang, Xinhai Chang +13D Reconstruction Depth Estimation
Wangxuan Institute of Computer Technology, Peking University Beijing Academy of Artificial Intelligence, Beijing, China · School of Computer Science and Technology, Beihang University · Yuanpei College, Peking University

Nov 24, 2024, cs.CVJ/K move · Enter open · S save

GSurf: Learning Signed Distance Fields from Splatting Opaque Gaussians for High-quality 3D Reconstruction

Baixin Xu, Jiangbei Hu, Jiaze Li, Ying He

College of Computing and Data Science, Nanyang Technological University · School of Software, Dalian University of Technology

3D Gaussian Splatting Signed Distance Functions 3D Reconstruction Multi-View 3D Reconstruction

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High-fidelity surface reconstruction from multi-view images is a core problem in 3D computer vision. While neural implicit surfaces like SDFs offer smooth geometry, they are often bottlenecked by the computational intensity of volume rendering. Conversely, 3D Gaussian Splatting (3DGS) provides rapid training but lacks geometry continuity, often leading to fragmented surfaces. This paper presents a novel framework that integrates Signed Distance Fields directly into the splatting pipeline. By leveraging the continuous nature of SDFs to regularize Gaussian primitives, our method effectively fills geometric holes and suppresses noise inherent in sparse point clouds. Unlike hybrid approaches that rely on heavy volumetric sampling, our approach utilizes the efficiency of splatting to achieve faster convergence. Extensive evaluations demonstrate that our method produces high-quality surfaces with significantly fewer primitives, offering a more compact and efficient representation for both indoor and outdoor environments.

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