用连续组件建模加速表面法向积分，大幅提升处理大图效率。
Towards Fast and Scalable Normal Integration using Continuous Components
- 将法向积分转为连续组件的尺度估计，减少优化变量数
- 在标准基准上仅需数秒即达当前最优结果，大图提速超10倍
- 适合需要快速重建高分辨率表面的应用场景
表面法向积分是计算机视觉中的基础问题，旨在从法向图重建表面。现有方法需迭代全局优化以联合估计每个像素的深度，难以扩展至大尺寸法向图。本文将法向积分重新建模为连续组件相对尺度的估计：约束同一组件内像素共同变化尺度，显著减少优化变量。框架包含组件初始估计的启发式策略、优化项重平衡机制以及组件迭代合并技术，进一步压缩问题规模。实验表明，该方法在标准法向积分基准上仅需数秒即可达到当前最优性能，在大分辨率法向图上的速度比像素级方法提升一个数量级。
原文摘要 · Abstract (English)
Surface normal integration is a fundamental problem in computer vision, dealing with the objective of reconstructing a surface from its corresponding normal map. Existing approaches require an iterative global optimization to jointly estimate the depth of each pixel, which scales poorly to larger normal maps. In this paper, we address this problem by recasting normal integration as the estimation of relative scales of continuous components. By constraining pixels belonging to the same component to jointly vary their scale, we drastically reduce the number of optimization variables. Our framework includes a heuristic to accurately estimate continuous components from the start, a strategy to rebalance optimization terms, and a technique to iteratively merge components to further reduce the size of the problem. Our method achieves state-of-the-art results on the standard normal integration benchmark in as little as a few seconds and achieves one-order-of-magnitude speedup over pixel-level approaches on large-resolution normal maps.
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