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We propose a scalable, efficient and accurate approach to retrieve 3D modelsfor objects in the wild. Our contribution is twofold. We first present a 3Dpose estimation approach for object categories which significantly outperformsthe state-of-the-art on Pascal3D+. Second, we use the estimated pose as a priorto retrieve 3D models which accurately represent the geometry of objects in RGBimages. For this purpose, we render depth images from 3D models under ourpredicted pose and match learned image descriptors of RGB images against thoseof rendered depth images using a CNN-based multi-view metric learning approach.In this way, we are the first to report quantitative results for 3D modelretrieval on Pascal3D+, where our method chooses the same models as humanannotators for 50% of the validation images on average. In addition, we showthat our method, which was trained purely on Pascal3D+, retrieves rich andaccurate 3D models from ShapeNet given RGB images of objects in the wild.

3D Pose Estimation and 3D Model Retrieval for Objects in the Wild