Acquiring 3D indoor environments with variability and repetition
Acquiring 3D indoor environments with variability and repetition
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DOI：
10.1145/2366145.2366157
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
Y. Kim;N. Mitra;Dong‐Ming Yan;L. Guibas
中科院分区：
文献类型：
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作者：
Y. Kim;N. Mitra;Dong‐Ming Yan;L. Guibas
到目前为止，大规模获取外部城市环境是一项成熟的技术，支持搜索、导航和商业中的许多应用。然而，室内环境就不是这样了，那里的出入经常受到限制，空间也很杂乱。此外，这种环境通常包含高密度的重复对象(例如，桌子、椅子、监视器等)。在有明显的姿势变化和发音的规则或非规则的安排中。在本文中，我们利用室内环境的特殊结构，利用低端手持扫描仪来加速其3D采集和识别。我们的方法分为两个阶段：(I)学习阶段，其中我们获取频繁出现的对象的3D模型，并仅从几次扫描中捕获它们的变化模式，以及(Ii)识别阶段，其中通过对新区域的单次扫描，我们以200ms/模型的平均识别时间识别先前见过的不同姿势和位置的对象。我们使用一系列具有挑战性的环境下的合成扫描和真实世界扫描来评估所提出的识别系统的稳健性和局限性。
Large-scale acquisition of exterior urban environments is by now a well-established technology, supporting many applications in search, navigation, and commerce. The same is, however, not the case for indoor environments, where access is often restricted and the spaces are cluttered. Further, such environments typically contain a high density of repeated objects (e.g., tables, chairs, monitors, etc.) in regular or non-regular arrangements with significant pose variations and articulations. In this paper, we exploit the special structure of indoor environments to accelerate their 3D acquisition and recognition with a low-end handheld scanner. Our approach runs in two phases: (i) a learning phase wherein we acquire 3D models of frequently occurring objects and capture their variability modes from only a few scans, and (ii) a recognition phase wherein from a single scan of a new area, we identify previously seen objects but in different poses and locations at an average recognition time of 200ms/model. We evaluate the robustness and limits of the proposed recognition system using a range of synthetic and real world scans under challenging settings.