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From Low-Cost Depth Sensors to CAD: Cross-Domain 3D Shape Retrieval via Regression Tree Fields
Yan Wang1, Jie Feng2, Zhixiang Wu2, Jun Wang3, and Shih-Fu Chang1, 2
1Dept. of Electrical Engineering, Columbia University, USA
yanwang@ee.columbia.edu
sfchang@ee.columbia.edu
2Dept. of Computer Science, Columbia University, USA
jiefeng@cs.columbia.edu
zw2229@columbia.edu
3IBM T. J. Watson Research Center, USA
wangjun@us.ibm.com
Abstract. The recent advances of low-cost and mobile depth sensors dramatically extend the potential of 3D shape retrieval and analysis. While the traditional research of 3D retrieval mainly focused on searching by a rough 2D sketch or with a high-quality CAD model, we tackle a novel and challenging problem of cross-domain 3D shape retrieval, in which users can use 3D scans from low-cost depth sensors like Kinect as queries to search CAD models in the database. To cope with the imperfection of user-captured models such as model noise and occlusion, we propose a cross-domain shape retrieval framework, which minimizes the potential function of a Conditional Random Field to efficiently generate the retrieval scores. In particular, the potential function consists of two critical components: one unary potential term provides robust cross-domain partial matching and the other pairwise potential term embeds spatial structures to alleviate the instability from model noise. Both potential components are efficiently estimated using random forests with 3D local features, forming a Regression Tree Field framework. We conduct extensive experiments on two recently released user-captured 3D shape datasets and compare with several state-of-the-art approaches on the cross-domain shape retrieval task. The experimental results demonstrate that our proposed method outperforms the competing methods with a significant performance gain.
LNCS 8689, p. 489 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014