Viewpoint-aware object detection and continuous pose estimation

We describe an approach to category-level detection and viewpoint estimation for rigid 3D objects from single 2D images. In contrast to many existing methods, we directly integrate 3D reasoning with an appearance-based voting architecture. Our method relies on a nonparametric representation of a joint distribution of shape and appearance of the object class. Our voting method employs a novel parameterization of joint detection and viewpoint hypothesis space, allowing efficient accumulation of evidence. We combine this with a re-scoring and refinement mechanism, using an ensemble of view-specific support vector machines. We evaluate the performance of our approach in detection and pose estimation of cars on a number of benchmark datasets. Finally we introduce the “Weizmann Cars ViewPoint” (WCVP) dataset, a benchmark for evaluating continuous pose estimation.

Figure optionsDownload high-quality image (116 K)Download as PowerPoint slideHighlights
► Viewpoint invariant detection and continuous pose estimation of rigid 3D objects.
► Model integrates 3D shape and 2D appearance of an object class.
► Efficient voting scheme to search in 6D transformation space.