ICRA 2012 Paper Abstract

Close

Paper TuC210.2

Lai, Kevin (University of Washington), Bo, Liefeng (University of Washington), Ren, Xiaofeng (Intel Labs), Fox, Dieter (University of Washington)

Detection-Based Object Labeling in 3D Scenes

Scheduled for presentation during the Interactive Session "Interactive Session TuC-2" (TuC210), Tuesday, May 15, 2012, 15:00−15:30, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on February 23, 2018

Keywords Recognition, Visual Learning, Personal Robots

Abstract

We propose a view-based approach for labeling objects in 3D scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3D scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3D shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2018 PaperCept, Inc.
Page generated 2018-02-23  15:53:55 PST  Terms of use