NeUDF:学习带有音量染的神经未签名距离场
相关实验视频
Updated: Sep 30, 2026

14:25
Determining 3D Flow Fields via Multi-camera Light Field Imaging
Published on: March 6, 2013
16.7K
IEEE transactions on pattern analysis and machine intelligence
|November 28, 2023
概括
NeUDF使用未签名距离函数 (UDF) 和新的神经染技术重建具有开放表面的3D形状. 这促进了复杂物体的多视图形状重建,超出了封闭表面.
科学领域:
- 计算机视觉和图形学
- 几何深度学习 几何深度学习
- 三维重建的3D重建
背景情况:
- 神经隐式染已经进行了先进的多视图形状重建.
- 使用符号距离函数 (SDF) 的现有方法仅限于封闭的表面.
- 现实世界中的物体通常具有开放的表面结构,这对当前的方法构成了挑战.
研究的目的:
- 引入一种新的神经染框架,NeUDF,能够重建具有任意拓的表面.
- 仅从多视图监控中进行重建,克服基于SDF的方法的局限性.
- 为了应对重建现实世界对象中常见的开放表面的挑战.
主要方法:
- NeUDF使用无符号距离函数 (UDF) 来灵活表示任意表面.
- 针对开放表面重建而适应的神经体积染的正式规则 (自相一致,不偏见,遮蔽意识).
- 开发了一个专为UDF量身定制的染重量函数和一个正常的规范化策略,以解决在开放表面的方向模两可.
主要成果:
- NeUDF成功地从多视图数据中重建具有任意拓的表面.
- 该方法在具有挑战性的数据集上显著优于最先进的技术,特别是那些具有开放边界的数据集.
- 在MGN和Deep Fashion 3D数据集上进行评估,在复杂形状重建中表现出卓越的性能.
结论:
- NeUDF为具有开放表面的对象的多视图形状重建提供了强大的解决方案.
- 该UDF表示和定制染技术克服了以前基于SDF的方法的局限性.
- 这项工作将神经染的适用性扩展到更广泛的现实世界3D对象.
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