考虑透视投影的深度与法向融合，提升3D重建精度
Perspective-aware fusion of incomplete depth maps and surface normals for accurate 3D reconstruction
- 引入透视感知的对数深度融合机制
- 在缺失深度区域利用法向信息补全
- 适用于单视角传感器的高精度3D重建
本文研究基于单视角相机的3D表面重建问题，针对通过结构光扫描和摄影测量立体等技术获取的深度图与表面法向图。提出一种透视感知的对数深度融合方法，通过显式建模透视投影效应，扩展了传统的正交梯度驱动型深度-法向融合方法，实现度量准确的3D重建。此外，该方法利用可用的表面法向信息对缺失的深度测量进行修复（inpainting）。在DiLiGenT-MV数据集上的实验验证了方法的有效性，并凸显了考虑透视投影的重要性。
原文摘要 · Abstract (English)
We address the problem of reconstructing 3D surfaces from depth and surface normal maps acquired by a sensor system based on a single perspective camera. Depth and normal maps can be obtained through techniques such as structured-light scanning and photometric stereo, respectively. We propose a perspective-aware log-depth fusion approach that extends existing orthographic gradient-based depth-normals fusion methods by explicitly accounting for perspective projection, leading to metrically accurate 3D reconstructions. Additionally, the method handles missing depth measurements by leveraging available surface normal information to inpaint gaps. Experiments on the DiLiGenT-MV data set demonstrate the effectiveness of our approach and highlight the importance of perspective-aware depth-normals fusion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。