Deep learning of directional truncated signed distance function for robust 3D object recognition

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	Deep learning of directional truncated signed distance function for robust 3D object recognition
	Cong Y(丛杨); Liu HS(刘洪森); Tian DY(田冬英); Tang YD(唐延东); Fan BJ(范保杰); Wang S(王帅)
	2017
会议名称	2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
会议日期	September 24-28, 2017
会议地点	Vancouver, Canada
页码	5934-5940
通讯作者	Cong Y(丛杨)
中文摘要	In this paper, we develop a novel 3D object recognition algorithm to perform detection and pose estimation jointly. We focus on analyzing the advantages of the 3D point cloud relative to the RGB-D image and try to eliminate the unpredictability of output values that inevitably occurs in regression tasks. To achieve this, we first adopt the Truncated Signed Distance Function (TSDF) to encode the point cloud and extract low compact discriminative feature via unsupervised deep learning network. This approach can not only eliminate the dense scale sampling for offline model training but also reduce the distortion by mapping the 3D shape to the 2D plane and overcome the dependence on color cues. Then, we train a Hough forests to achieve multi-object detection and 6-DoF pose estimation simultaneously. In addition, we propose a robust multilevel verification strategy that effectively reduces the unpredictability of output values which occurs in the hough regression module. Experiments on public datasets demonstrate that our approach provides effective results comparable to the state-of-the-arts.
收录类别	EI ; CPCI（ISTP）
产权排序	1
会议主办者	AIRA; Amazon; Bosch; Clearpath; et al.; Guangdong University of Technology
会议录	IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISSN号	2153-0858
ISBN号	978-1-5386-2682-5
WOS记录号	WOS:000426978205082
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/21344]
专题	沈阳自动化研究所_机器人学研究室
作者单位	1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China 2.University of Chinese Academy of Sciences, China
推荐引用方式 GB/T 7714	Cong Y,Liu HS,Tian DY,et al. Deep learning of directional truncated signed distance function for robust 3D object recognition[C]. 见:2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017. Vancouver, Canada. September 24-28, 2017.