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Object Discovery in 3D scenes via Shape Analysis
| Citations: | 25 - 0 self |
BibTeX
@MISC{Karpathy_objectdiscovery,
author = {Andrej Karpathy and Stephen Miller and Li Fei-fei},
title = {Object Discovery in 3D scenes via Shape Analysis},
year = {}
}
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Abstract
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. I.
