3-D Scene Analysis via Sequenced Predictions over Points and Regions
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.
Xuehan Xiong Daniel Munoz J. Andrew Bagnell Martial Hebert
The Robotics Institute Carnegie Mellon University
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
2609-2616
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）