In many scenarios, robots encounter rotationally symmetric objects for which no known 3D model exists. To be able to grasp such objects using existing grasp point computation schemes, an estimate of their 3D-pose and shape is necessary. In this paper, we address the problem of recovering 3D-pose and shape of an unknown surface of revolution from two perspective views of known relative orientation. We propose a new algorithm for simultaneous estimation of pose and shape without making use of any cross-sections or bi-tangent points needed by other approaches. Our algorithm builds upon existing single-view SOR reconstruction approaches and couples the pose estimation and reconstruction process. Pose is optimized to minimize discrepancies between reconstructed shapes from two views. Our method works even in the presence of only one of the two apparent contours of a surface of revolution. We test our algorithm by comparing to ground-truth poses and shapes as well as performing grasping experiments. We introduce a new dataset of rotationally symmetric objects in a variety of poses and backgrounds and with a measured ground-truth pose and shape.

We present a novel approach for detecting objects and estimating their 3D pose in single images of cluttered scenes. Objects are given in terms of 3D models without accompanying texture cues. A deformable parts-based model is trained on clusters of silhouettes of similar poses and produces hypotheses about possible object locations at test time. Objects are simultaneously segmented and verified inside each hypothesis bounding region by selecting the set of superpixels whose collective shape matches the model silhouette. A final iteration on the 6-DOF object pose minimizes the distance between the selected image contours and the actual projection of the 3D model. We demonstrate successful grasps using our detection and pose estimate with a PR2 robot. Extensive evaluation with a novel ground truth dataset shows the considerable benefit of using shape-driven cues for detecting objects in heavily cluttered scenes.

Natural scene classification is a fundamental challenge in computer vision. By far, the majority of studies have limited their scope to scenes from single image stills and thereby ignore potentially informative temporal cues. The current paper is concerned with determining the degree of performance gain in considering short videos for recognizing natural scenes. Towards this end, the impact of multiscale orientation measurements on scene classification is systematically investigated, as related to: (i) spatial appearance, (ii) temporal dynamics and (iii) joint spatial appearance and dynamics. These measurements in visual space, x-y, and spacetime, x-y-t, are recovered by a bank of spatiotemporal oriented energy filters. In addition, a new data set is introduced that contains 420 image sequences spanning fourteen scene categories, with temporal scene information due to objects and surfaces decoupled from camera-induced ones. This data set is used to evaluate classification performance of the various orientation-related representations, as well as state-of-the-art alternatives. It is shown that a notable performance increase is realized by spatiotemporal approaches in comparison to purely spatial or purely temporal methods.