FRA-NBV：一种快速且感知反射特性的最优下一个视点策略
FRA-NBV: A Fast and Reflectivity-Aware Next-Best-View Strategy
浏览论文内容
中文总结 AI 辅助
FRA-NBV策略针对反射表面导致的深度损失问题，通过识别反射区域并调整传感器位姿，提升工业场景下自主三维重建的覆盖率。
中文摘要 AI 辅助
采用深度传感器的自主三维重建受反射表面影响极大，这类表面会导致测量数据缺失或不可靠，降低传统最优下一个视点（NBV）策略的有效性。在涉及反射部件、采用低成本低分辨率深度传感的工业应用中，对传感故障的鲁棒性要求极高，该问题的局限性尤为关键。本文提出一种快速感知反射特性的最优下一个视点（FRA-NBV）策略，该策略明确解决反射导致的深度损失问题，无需依赖先验物体模型或材料反射率假设，适用于多种工业场景。通过缺失深度测量的空间分布识别反射区域，利用在线基于椭球的物体估计表示将其定位在三维空间中。随后采用恢复策略选择额外位姿，调整传感器入射角以提升受影响区域的重建概率。对不同几何与反射复杂度物体的实验表明，该方法在真实工业条件下可显著提升重建覆盖率。
英文摘要
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.
发表机构
- Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。
