提出新方法显式建模深度不连续，支持任意中心相机的法向积分。
Discontinuity-aware Normal Integration for Generic Central Camera Models
- 基于局部平面假设，用法向与射线方向约束建模不连续性
- 在标准基准上达到当前最优效果，首次直接处理通用中心相机
- 适合需要高精度三维重建的视觉算法研究者
从表面法向图恢复三维表面（即法向积分）是基于亮度的三维重建技术（如形状-光照和光度立体）的关键步骤。现有方法大多隐式处理深度不连续性，且仅适用于正交或理想针孔相机。本文提出一种新公式，可显式建模不连续性并支持通用中心相机模型。核心思想基于局部平面假设，通过表面法向与射线方向之间的约束进行建模。相比现有方法，本方案更准确地逼近深度与法向的关系，在标准法向积分基准上取得当前最优结果，且是首个能直接处理通用中心相机模型的方法。
原文摘要 · Abstract (English)
Recovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-shading and photometric stereo. The vast majority of existing approaches for normal integration handle only implicitly the presence of depth discontinuities and are limited to orthographic or ideal pinhole cameras. In this paper, we propose a novel formulation that allows modeling discontinuities explicitly and handling generic central cameras. Our key idea is based on a local planarity assumption, that we model through constraints between surface normals and ray directions. Compared to existing methods, our approach more accurately approximates the relation between depth and surface normals, achieves state-of-the-art results on the standard normal integration benchmark, and is the first to directly handle generic central camera models.
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