基于隐式神经表示的室内场景的三维重建
Zhaoji Lin1, Yutao Huang2, Li Yao2
1School of Computer Science and Engineering, Sanjiang University, Nanjing 210012, China.
Journal of imaging
|September 27, 2024
概括
本研究介绍了一种新的3D室内场景重建方法,该方法结合了神经辐射场 (NeRF) 和符号距离函数 (SDF). 这种方法增强了几何细节,并消除了优质3D模型的噪音.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 3D场景重建 3D场景重建
背景情况:
- 传统的3D重建方法在室内场景中与表面细节,大质感和不均的照明作斗争.
- 现有的方法往往会产生杂的3D模型与漂浮的碎片.
研究的目的:
- 为室内场景开发一种先进的3D重建方法.
- 克服传统方法在细节保护和降噪方面的局限性.
主要方法:
- 结合了神经辐射场 (NeRF) 与符号距离函数 (SDF) 隐性表示.
- 使用NeRF体积密度用于SDF几何信息.
- 包含适应性正常预先优化,用于增强形状和表面学习.
- 引入了一个新的规范化术语,用于重量分配和消除噪音.
主要成果:
- 从NeRFs获得高质量的几何信息保存.
- 使用SDFs生成显式网格与光滑表面.
- 显著改善了大型平面纹理和不均的照明区域的重建.
- 从重建模型中有效消除漂浮碎片噪音.
结论:
- 拟议的NeRF-SDF混合方法提供了卓越的3D室内场景重建.
- 在ScanNet,Hypersim和Replica数据集上展示了最先进的性能.
- 提供了一个强大的解决方案,用于详细和干净的3D室内场景建模.
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