To carry out autonomous 3D scanning and online reconstruction of unknown
indoor scenes, one has to find a balance between global exploration of the
entire scene and local scanning of the objects within it. In this work, we
propose a novel approach, which provides object-aware guidance for
autoscanning, for exploring, reconstructing, and understanding an unknown scene
within one navigation pass. Our approach interleaves between object analysis to
identify the next best object (NBO) for global exploration, and object-aware
information gain analysis to plan the next best view (NBV) for local scanning.
First, an objectness-based segmentation method is introduced to extract
semantic objects from the current scene surface via a multi-class graph cuts
minimization. Then, an object of interest (OOI) is identified as the NBO which
the robot aims to visit and scan. The robot then conducts fine scanning on the
OOI with views determined by the NBV strategy. When the OOI is recognized as a
full object, it can be replaced by its most similar 3D model in a shape
database. The algorithm iterates until all of the objects are recognized and
reconstructed in the scene. Various experiments and comparisons have shown the
feasibility of our proposed approach.

提出了一种自主三维扫描的新方法，它通过对象分析确定全局探测的下一个最佳对象（NBO），并通过对象感知信息分析来规划局部扫描的下一个最佳视图（NBV），从而实现了对未知场景的探索、重构和理解。