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 search- ing 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 imper- fection 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 gener- ate 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 po- tential components are efficiently estimated using random forests with 3D local features, forming a Regression Tree Field framework. We con- duct 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 demon- strate that our proposed method outperforms the competing methods with a significant performance gain.