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3-D Scene Analysis via Sequenced Predictions over Points and Regions

Xuehan Xiong, Daniel Munoz, J. Andrew (Drew) Bagnell and Martial Hebert
Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation (ICRA), May, 2011

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Abstract

We address the problem of understanding scenes from 3-D laser scans via per-point assignment of semantic labels. In order to mitigate the difficulties of using a graphical model for modeling the contextual relationships among the 3-D points, we instead propose a multi-stage inference procedure to capture these relationships. More specifically, we train this procedure to use point cloud statistics and learn relational information (e.g., tree-trunks are below vegetation) over fine (point-wise) and coarse (region-wise) scales. We evaluate our approach on three different datasets, that were obtained from different sensors, and demonstrate improved performance.

BibTeX Reference
@conference{Xiong-2011-7246,
title = {3-D Scene Analysis via Sequenced Predictions over Points and Regions},
author = {Xuehan Xiong and Daniel Munoz and J. Andrew (Drew) Bagnell and Martial Hebert},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
sponsor = {ARL-Collaborative Technology Alliance Program, QinetiQ North America Robotics Fellowship},
school = {Robotics Institute , Carnegie Mellon University},
month = {May},
year = {2011},
address = {Pittsburgh, PA},
}
2017-09-13T10:40:23+00:00