Object discovery in 3D scenes via shape analysis
Robotics and Automation(2013)
摘要
We present a method for discovering object models from 3D meshes of indoor environments. Our algorithm first decomposes the scene into a set of candidate mesh segments and then ranks each segment according to its “objectness” - a quality that distinguishes objects from clutter. To do so, we propose five intrinsic shape measures: compactness, symmetry, smoothness, and local and global convexity. We additionally propose a recurrence measure, codifying the intuition that frequently occurring geometries are more likely to correspond to complete objects. We evaluate our method in both supervised and unsupervised regimes on a dataset of 58 indoor scenes collected using an Open Source implementation of Kinect Fusion [1]. We show that our approach can reliably and efficiently distinguish objects from clutter, with Average Precision score of .92. We make our dataset available to the public.
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关键词
object detection,object recognition,shape recognition,3D indoor environment meshes,3D scenes,Kinect Fusion,average precision score,compactness,frequently occurring geometries,global convexity,intrinsic shape measures,local convexity,object model discovery,open source implementation,shape analysis,smoothness,symmetry
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