Abstract

Reconstructing 3D objects from scanned measurements is a fundamental task in computer vision. A central factor for the effectiveness of 3D reconstruction is the selection of sensor views for scanning. The latter remains an open problem in the 3D geometry processing area, known as the next-best-view planning problem, and is commonly approached by combinatorial or greedy methods. In this work, we propose a reinforcement learning-based approach to sequential next-best-view planning. The method is implemented based on the gym environment including 3D reconstruction, next-best-scan planning, and image acquisition features. We demonstrate this method to outperform the baselines in terms of the number of required scans and the obtained 3D mesh reconstruction accuracy.

中文翻译：

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增强学习的下一个最佳视图计划，用于扫描任意3D形状

摘要

从扫描的测量结果重建3D对象是计算机视觉的基本任务。3D重建有效性的一个中心因素是扫描传感器视图的选择。后者在3D几何处理领域仍然是一个未解决的问题，被称为次佳计划问题，通常通过组合或贪婪方法来解决。在这项工作中，我们提出了一种基于强化学习的方法来进行顺序的最佳视图计划。该方法基于包括3D重建，次佳扫描计划和图像采集功能在内的体育馆环境实现。我们证明了该方法在所需扫描次数和获得的3D网格重建精度方面均优于基线。