Header logo is

5 results (View BibTeX file of all listed publications)

2014

Thumb xl thumb schoenbein2014iros
Omnidirectional 3D Reconstruction in Augmented Manhattan Worlds

Schoenbein, M., Geiger, A.

International Conference on Intelligent Robots and Systems, pages: 716 - 723, IEEE, Chicago, IL, USA, IEEE/RSJ International Conference on Intelligent Robots and System, October 2014 (conference)

Abstract
This paper proposes a method for high-quality omnidirectional 3D reconstruction of augmented Manhattan worlds from catadioptric stereo video sequences. In contrast to existing works we do not rely on constructing virtual perspective views, but instead propose to optimize depth jointly in a unified omnidirectional space. Furthermore, we show that plane-based prior models can be applied even though planes in 3D do not project to planes in the omnidirectional domain. Towards this goal, we propose an omnidirectional slanted-plane Markov random field model which relies on plane hypotheses extracted using a novel voting scheme for 3D planes in omnidirectional space. To quantitatively evaluate our method we introduce a dataset which we have captured using our autonomous driving platform AnnieWAY which we equipped with two horizontally aligned catadioptric cameras and a Velodyne HDL-64E laser scanner for precise ground truth depth measurements. As evidenced by our experiments, the proposed method clearly benefits from the unified view and significantly outperforms existing stereo matching techniques both quantitatively and qualitatively. Furthermore, our method is able to reduce noise and the obtained depth maps can be represented very compactly by a small number of image segments and plane parameters.

avg ps

pdf DOI [BibTex]

2014

avg ps Schoenbein, M., Geiger, A. Omnidirectional 3D Reconstruction in Augmented Manhattan Worlds International Conference on Intelligent Robots and Systems, pages: 716 - 723, IEEE, Chicago, IL, USA, IEEE/RSJ International Conference on Intelligent Robots and System, October 2014 (conference)

pdf DOI [BibTex]

Thumb xl roser
Simultaneous Underwater Visibility Assessment, Enhancement and Improved Stereo

Roser, M., Dunbabin, M., Geiger, A.

IEEE International Conference on Robotics and Automation, pages: 3840 - 3847 , Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)

Abstract
Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows real-time implementation onboard an Autonomous Underwater Vehicles for improved navigation and obstacle avoidance performance.

avg ps

pdf DOI [BibTex]

avg ps Roser, M., Dunbabin, M., Geiger, A. Simultaneous Underwater Visibility Assessment, Enhancement and Improved Stereo IEEE International Conference on Robotics and Automation, pages: 3840 - 3847 , Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)

pdf DOI [BibTex]

Thumb xl schoenbein
Calibrating and Centering Quasi-Central Catadioptric Cameras

Schoenbein, M., Strauss, T., Geiger, A.

IEEE International Conference on Robotics and Automation, pages: 4443 - 4450, Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)

Abstract
Non-central catadioptric models are able to cope with irregular camera setups and inaccuracies in the manufacturing process but are computationally demanding and thus not suitable for robotic applications. On the other hand, calibrating a quasi-central (almost central) system with a central model introduces errors due to a wrong relationship between the viewing ray orientations and the pixels on the image sensor. In this paper, we propose a central approximation to quasi-central catadioptric camera systems that is both accurate and efficient. We observe that the distance to points in 3D is typically large compared to deviations from the single viewpoint. Thus, we first calibrate the system using a state-of-the-art non-central camera model. Next, we show that by remapping the observations we are able to match the orientation of the viewing rays of a much simpler single viewpoint model with the true ray orientations. While our approximation is general and applicable to all quasi-central camera systems, we focus on one of the most common cases in practice: hypercatadioptric cameras. We compare our model to a variety of baselines in synthetic and real localization and motion estimation experiments. We show that by using the proposed model we are able to achieve near non-central accuracy while obtaining speed-ups of more than three orders of magnitude compared to state-of-the-art non-central models.

avg ps

pdf DOI [BibTex]

avg ps Schoenbein, M., Strauss, T., Geiger, A. Calibrating and Centering Quasi-Central Catadioptric Cameras IEEE International Conference on Robotics and Automation, pages: 4443 - 4450, Hong Kong, China, IEEE International Conference on Robotics and Automation, June 2014 (conference)

pdf DOI [BibTex]

Thumb xl pami
3D Traffic Scene Understanding from Movable Platforms

Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 36(5):1012-1025, published, IEEE, Los Alamitos, CA, May 2014 (article)

Abstract
In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particular, the scene topology, geometry and traffic activities are inferred from short video sequences. Inspired by the impressive driving capabilities of humans, our model does not rely on GPS, lidar or map knowledge. Instead, it takes advantage of a diverse set of visual cues in the form of vehicle tracklets, vanishing points, semantic scene labels, scene flow and occupancy grids. For each of these cues we propose likelihood functions that are integrated into a probabilistic generative model. We learn all model parameters from training data using contrastive divergence. Experiments conducted on videos of 113 representative intersections show that our approach successfully infers the correct layout in a variety of very challenging scenarios. To evaluate the importance of each feature cue, experiments using different feature combinations are conducted. Furthermore, we show how by employing context derived from the proposed method we are able to improve over the state-of-the-art in terms of object detection and object orientation estimation in challenging and cluttered urban environments.

avg ps

pdf link (url) [BibTex]

avg ps Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R. 3D Traffic Scene Understanding from Movable Platforms IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 36(5):1012-1025, published, IEEE, Los Alamitos, CA, May 2014 (article)

pdf link (url) [BibTex]

no image
Geometric Image Synthesis

Alhaija, H. A., Mustikovela, S. K., Geiger, A., Rother, C.

(conference)

avg

Project Page [BibTex]

avg Alhaija, H. A., Mustikovela, S. K., Geiger, A., Rother, C. Geometric Image Synthesis (conference)

Project Page [BibTex]