Journal
ACM TRANSACTIONS ON GRAPHICS
Volume 37, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3197517.3201295
Keywords
Indoor scene reconstruction; autonomous reconstruction; next-best-object; next-best-view
Categories
Funding
- NSFC [61672482, 61672481, 61622212, 61572507, 61532003, 61522213]
- One Hundred Talent Project of the Chinese Academy of Sciences, 973 Program [2015CB35 2501]
- Guangdong Science and Technology Program [2015A03031201 5]
- Shenzhen Science and Technology Program [KQJSCX20170727 101233642, JCYJ20151015151249564]
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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.
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