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We present a novel method for recovering the 3D structure and scene flow from calibrated multi-view sequences. We propose a 3D point cloud parametrization of the 3D structure and scene flow that allows us to directly estimate the desired unknowns. A unified global energy functional is proposed to incorporate the information from the available sequences and simultaneously recover both depth and scene flow. The functional enforces multi-view geometric consistency and imposes brightness constancy and piecewise smoothness assumptions directly on the 3D unknowns. It inherently handles the challenges of discontinuities, occlusions, and large displacements. The main contribution of this work is the fusion of a 3D representation and an advanced variational framework that directly uses the available multi-view information. This formulation allows us to advantageously bind the 3D unknowns in time and space. Different from optical flow and disparity, the proposed method results in a nonlinear mapping between the images' coordinates, thus giving rise to additional challenges in the optimization process. Our experiments on real and synthetic data demonstrate that the proposed method successfully recovers the 3D structure and scene flow despite the complicated nonconvex optimization problem. [ABSTRACT FROM AUTHOR]

Object detection is one of the key components in modern computer vision systems. While the detection of a specific rigid object under changing viewpoints was considered hard just a few years ago, current research strives to detect and recognize classes of non-rigid, articulated objects. Hampered by the omnipresent confusing information due to clutter and occlusion, the focus has shifted from holistic approaches for object detection to representations of individual object parts linked by structural information, along with richer contextual descriptions of object configurations. Along this line of research, we present a practicable and expandable probabilistic framework for parts-based object class representation, enabling the detection of rigid and articulated object classes in arbitrary views. We investigate learning of this representation from labelled training images and infer globally optimal solutions to the contextual MAP-detection problem, using A*-search with a novel lower-bound as admissible heuristic. An assessment of the inference performance of Belief-Propagation and Tree-Reweighted Belief Propagation is obtained as a by-product. The generality of our approach is demonstrated on four different datasets utilizing domain dependent information cues. [ABSTRACT FROM AUTHOR]