Aljosa Osep

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Raquel Urtasun14
Andreas Geiger14
Bastian Leibe13
Kyoung Mu Lee11
Sang Uk Lee11
8Bastian Leibe
7Michael Weinmann
6Reinhard Klein
3Dirk Klostermann
3Jörg Stückler

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Multi-view Normal Field Integration for 3D Reconstruction of Mirroring Objects
In this paper, we present a novel, robust multi-view normal field integration technique for reconstructing the full 3D shape of mirroring objects. We employ a turntable-based setup with several cameras and displays. These are used to display illumination patterns which are reflected by the object surface. The pattern information observed in the cameras… (More)
Fusing Structured Light Consistency and Helmholtz Normals for 3D Reconstruction
In this paper, we propose a 3D reconstruction approach which combines a structured light based consistency measure with dense normal information obtained by exploiting the Helmholtz reciprocity principle. This combination compensates for the individual limitations of techniques providing normal information, which are mainly affected by low-frequency drift,… (More)
Multi-scale object candidates for generic object tracking in street scenes
Most vision based systems for object tracking in urban environments focus on a limited number of important object categories such as cars or pedestrians, for which powerful detectors are available. However, practical driving scenarios contain many additional objects of interest, for which suitable detectors either do not yet exist or would be cumbersome to… (More)
Scene flow propagation for semantic mapping and object discovery in dynamic street scenes
Scene understanding is an important prerequisite for vehicles and robots that operate autonomously in dynamic urban street scenes. For navigation and high-level behavior planning, the robots not only require a persistent 3D model of the static surroundings-equally important, they need to perceive and keep track of dynamic objects. In this paper, we propose… (More)
A fixed-dimensional 3D shape representation for matching partially observed objects in street scenes
In this paper, we present an object-centric, fixed-dimensional 3D shape representation for robust matching of partially observed object shapes, which is an important component for object categorization from 3D data. A main problem when working with RGB-D data from stereo, Kinect, or laser sensors is that the 3D information is typically quite noisy. For that… (More)
Unsupervised Learning of Shape-Motion Patterns for Objects in Urban Street Scenes
Tracking in urban street scenes is predominantly based on pretrained object-specific detectors and Kalman filter based tracking. More recently, methods have been proposed that track objects by modelling their shape, as well as ones that predict the motion of objects using learned trajectory models. In this paper, we combine these ideas and propose… (More)
Combined image- and world-space tracking in traffic scenes
Tracking in urban street scenes plays a central role in autonomous systems such as self-driving cars. Most of the current vision-based tracking methods perform tracking in the image domain. Other approaches, e.g. based on LIDAR and radar, track purely in 3D. While some vision-based tracking methods invoke 3D information in parts of their pipeline, and some… (More)
Multiview Normal Field Integration using Graph-Cuts
While there are many algorithms which address the reconstruction of partial surfaces from single-view normal fields, to the best of our knowledge, there is only one method [Chang et al., 'Multiview Normal Field Integration using Level Sets', CVPR 2007] which focuses on the reconstruction of the full shape of an object from multiple normal fields captured… (More)
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