针对反光导致的三维重建缺失，提出快速感知反光的最优视角策略。
FRA-NBV: A Fast and Reflectivity-Aware Next-Best-View Strategy
- 基于缺失深度数据的空间分布，实时定位反光区域。
- 通过调整传感器入射角，在真实工业场景中提升重建覆盖率。
- 无需先验模型，适用于多种工业设备配置。
使用深度传感器进行自主三维重建时，反光表面会导致测量缺失或不可靠，降低传统最佳视角（NBV）策略的效果。这一问题在涉及反光部件且使用低成本、低分辨率深度传感的工业应用中尤为严重，对传感故障的鲁棒性要求极高。本文提出一种快速反光感知的最优视角（FRA-NBV）策略，明确应对由反光引起的深度丢失问题，且不依赖物体先验模型或材料反射特性假设，适用于广泛的工业配置。反光区域通过缺失深度测量的空间分布识别，并利用在线椭球体表示法在三维空间中定位。随后的恢复策略选择额外观测位姿，以改变传感器入射角，提高重建受影响区域的可能性。在具有不同几何与反射复杂度的物体上进行实验，结果表明该方法在真实工业条件下显著提升了重建覆盖范围。
原文摘要 · Abstract (English)
Autonomous 3D reconstruction with depth sensors is strongly affected by reflective surfaces, which cause missing or unreliable measurements and reduce the effectiveness of conventional Next-Best-View (NBV) strategies. This limitation is particularly critical in industrial applications involving reflective components and low-cost, low-resolution depth sensing, where robustness to sensing failures is essential. This paper proposes a Fast Reflectivity-Aware Next-Best-View (FRA-NBV) strategy that explicitly addresses reflection-induced depth loss without relying on prior object models or assumptions on material reflectance, making it suitable for a wide range of industrial configurations. Reflective regions are identified from the spatial distribution of missing depth measurements and localized in three-dimensional space using an online ellipsoid-based representation of the object estimate. A recovery strategy then selects additional poses that modify the sensor's angle of incidence to improve the likelihood of reconstructing the affected regions. Experiments on objects with different geometric and reflective complexity demonstrate that the approach significantly improves reconstruction coverage under realistic industrial conditions.
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