HSDF: Hybrid Sign and Distance Field for Neural Representation of Surfaces With Arbitrary Topologies
文献类型：期刊论文
| 作者 | Wang, Li1,2; Liu, Yu-Tao1,2; Yang, Jie1; Chen, Weikai3; Meng, Xiaoxu3; Yang, Bo3; Li, Jintao1,2; Gao, Lin1,2 |
| 刊名 | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
 |
| 出版日期 | 2025-09-01 |
| 卷号 | 31期号:9页码:5215-5228 |
| 关键词 | Shape Surface reconstruction Three-dimensional displays Rendering (computer graphics) Topology Surface treatment Geometry Implicit field point cloud reconstruction signed distance field unsigned distance field |
| ISSN号 | 1077-2626 |
| DOI | 10.1109/TVCG.2024.3443508 |
| 英文摘要 | Neural implicit function based on signed distance field (SDF) has achieved impressive progress in reconstructing 3D models with high fidelity. However, such approaches can only represent closed surfaces. Recent works based on unsigned distance function (UDF) are proposed to handle both watertight and single-layered open surfaces. Nonetheless, as UDF is signless, its direct output is limited to the point cloud, which imposes an additional challenge on extracting high-quality meshes from discrete points. To address this challenge, we present a novel neural implicit representation coded HSDF, which is a hybrid of signed and unsigned distance fields. In particular, HSDF is able to represent arbitrary topologies containing both closed and open surfaces while being compatible with existing iso-surface extraction techniques for easy field-to-mesh conversion. In addition to predicting a UDF, we propose to learn an additional sign field. Unlike traditional SDF, HSDF is able to locate the surface of interest before level surface extraction by generating surface points following NDF (Chibane et al. 2020). We are then able to obtain open surfaces via an adaptive meshing approach that only instantiates regions containing surfaces into a polygon mesh. HSDF benefits downstream tasks like neural rendering, as it enables the rendering of back-faces of open surfaces. We also propose HSDF-Net, a dedicated learning framework that factorizes the learning of HSDF into two easier sub-problems. Experiments and evaluations show that HSDF outperforms the state-of-the-art techniques both qualitatively and quantitatively on some of the used datasets. |
| 资助项目 | CCF-Tencent Open Fund ; Beijing Municipal Natural Science Foundation for Distinguished Young Scholars[JQ21013] ; National Natural Science Foundation of China[62322210] ; National Natural Science Foundation of China[62061136007] ; National Natural Science Foundation of China[61872440] ; Royal Society Newton Advanced Fellowship[NAF\R2\192151] ; Youth Innovation Promotion Association CAS |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001542452400043 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41986]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Gao, Lin |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Tencent Games, DCC Algorithm Res Ctr, Los Angeles, CA 90066 USA |
| 推荐引用方式 GB/T 7714 | Wang, Li,Liu, Yu-Tao,Yang, Jie,et al. HSDF: Hybrid Sign and Distance Field for Neural Representation of Surfaces With Arbitrary Topologies[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2025,31(9):5215-5228. |
| APA | Wang, Li.,Liu, Yu-Tao.,Yang, Jie.,Chen, Weikai.,Meng, Xiaoxu.,...&Gao, Lin.(2025).HSDF: Hybrid Sign and Distance Field for Neural Representation of Surfaces With Arbitrary Topologies.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,31(9),5215-5228. |
| MLA | Wang, Li,et al."HSDF: Hybrid Sign and Distance Field for Neural Representation of Surfaces With Arbitrary Topologies".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 31.9(2025):5215-5228. |
入库方式： OAI收割
来源：计算技术研究所
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