Abstract-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.
Another segmentation-based approach based on shape analysis using compactness, symmetry, smoothness, and local and global convexity of segments and their recurrence is proposed in REF .
Object discovery in 3D scenes via shape analysis
2013 IEEE International Conference on Robotics and Automation
2013 IEEE International Conference on Robotics and Automation
Mathematics Computer Science