ICRA 2011 Paper Abstract


Paper WeA104.3

Xiong, Xuehan (Carnegie Mellon University), Munoz, Daniel (Carnegie Mellon University), Bagnell, James (Carnegie Mellon University), hebert, martial (CMU)

3-D Scene Analysis Via Sequenced Predictions Over Points and Regions

Scheduled for presentation during the Regular Sessions "Range Sensing I" (WeA104), Wednesday, May 11, 2011, 08:50−09:05, Room 3E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Visual Learning, Recognition, Range Sensing


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.



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