Detection-based Object Labeling in 3D Scenes

被引：0

作者：

Lai, Kevin ^{[1
]}

Bo, Liefeng ^{[1
]}

Ren, Xiaofeng

Fox, Dieter ^{[1
]}

机构：

[1] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) | 2012年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1330 / 1337

页数：8