High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the NBV planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity, and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer, a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm.

翻译：高质量的真实世界观测对于多种应用至关重要，包括生成小尺度场景的3D打印复制品以及进行大规模基础设施的检测。这些3D观测通常通过结合来自不同视角的多个传感器测量数据获得。引导合适视角的选择被称为下一最佳视角（NBV）规划问题。大多数NBV方法使用刚性数据结构（如表面网格或体素网格）对测量进行推理。这简化了下一最佳视角的选择，但可能计算成本高、降低真实世界保真度，并将下一最佳视角的选择与最终数据处理耦合在一起。本文提出了表面边缘探索器（SEE），一种无需刚性数据结构即可直接从先前传感器测量中选择新观测的NBV方法。SEE利用测量密度提出新的下一最佳视角，以增加对观测不足表面的覆盖，同时避免潜在的遮挡。模拟实验的统计结果表明，与评估的基于体素的方法相比，SEE在覆盖相近或更好表面覆盖的同时，在小型和大型场景中均能减少观测时间和移动距离。实际实验展示了SEE使用固定在机械臂上的3D传感器自主观测一个鹿雕像。