3D²PM – 3D Deformable Part Models

Bojan Pepik¹, Peter Gehler³, Michael Stark^{1, 2}, and Bernt Schiele¹

¹Max Planck Institute for Informatics, Germany

²Stanford University, USA

³Max Planck Institute for Intelligent Systems, Germany

Abstract. As objects are inherently 3-dimensional, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 2D feature-based models are the predominant paradigm in object recognition today. While such models have shown competitive bounding box (BB) detection performance, they are clearly limited in their capability of fine-grained reasoning in 3D or continuous viewpoint estimation as required for advanced tasks such as 3D scene understanding. This work extends the deformable part model [1] to a 3D object model. It consists of multiple parts modeled in 3D and a continuous appearance model. As a result, the model generalizes beyond BB oriented object detection and can be jointly optimized in a discriminative fashion for object detection and viewpoint estimation. Our 3D Deformable Part Model (3D²PM) leverages on CAD data of the object class, as a 3D geometry proxy.

LNCS 7577, p. 356 ff.

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