Estimating Spatial Layout of Cluttered Rooms by Using Object Prior and Spatial Constraints
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摘要: 对结构化室内场景的空域布局结构进行估计是计算机视觉领域的研究热点之一.然而，对于内部堆放了众多杂乱物体的室内场景，现有的大多数方法容易受到各种物体遮挡的影响而无法对这一类场景的布局结构进行准确推理.为此，本文方法充分考虑了房间和物体之间的几何和语义关联性，参数化地对房间和内部物体的三维体积分别进行描述，并且提出利用多种高层图像语义来获取物体的先验信息.此外，还在此基础上加入了空域排他性和空域位置等多种空域约束，进而在改进室内场景空域布局估计的同时为物体的识别和定位提供关键信息.本文方法不仅具有较低的求解复杂度，而且通过试验表明相比于现有的经典方法在杂乱的室内场景中能够取得更为鲁棒的空域布局推理结果.Abstract: Estimating spatial layout of a structural indoor scene is one of the research hotspots in computer vision. However, most of the current solutions cannot work robustly in a cluttered room due to occlusion of different objects inside. In this paper, a new algorithm which integrates geometric and semantic relations between room and objects is proposed to recover the spatial layout of a cluttered room. This algorithm parametrically represents the 3D volume of both room and objects and uses multiple high-level image semantics to obtain object priors. Furthermore, several spatial constraints such as spatial exclusion and containment are used which simultaneously optimize spatial layout estimation of the room and provide significant information for object recognition and localization. One advantage of the algorithm is its low computational complexity, and experimental results also demonstrate that it can work more robustly in cluttered rooms than several classic algorithms.1) 本文责任编委 贾云得
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图 5 基于不同高层图像语义的物体位置估计
Fig. 5 Different high-level image semantic based object localization
图 10 不同高层图像语义在物体结构假设中的像素误差和物体识别率
Fig. 10 The pixel error and object recognition rate of different high-level image semantics in object structure hypothesis
表 1 房间结构假设误差
Table 1 Room hypothesis error
方法(OPP) A1 A2 A3 A4 Pixel error 21.2 26.8 17.0 15.9 Corner error 6.3 11.4 5.5 5.0 
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