Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Learning and Understanding in 3D Point Clouds

Scientific Director: Tolga Birdal, Slobodan Ilic

Contact Person(s): Tolga Birdal

In industrial collaboration with:
Siemens AG

Keywords: Registration / Visualization, Reconstruction, Computer Vision, Sensing


Point offer are a unified representation for 3D data. Whether the sensor is a laser scanner, lidar or a depth camera, the acquired information can be stored in form an unstructured point cloud. This makes such representation ubiquitous. In this project, we aim to adapt state-of-the-art learning algorithms to efficiently handle point cloud data in order to find objects, detect structures, perform segmentation and 3d reconstruction.


We present a revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching. In particular, we propose to couple the object detection with a coarse-to-fine segmentation, where each segment is subject to disjoint pose estimation. During matching, we apply a weighted Hough voting and an interpolated recovery of pose parameters. Finally, all the generated hypotheses are tested via an occlusion-aware ranking and sorted. We argue that such a combined pipeline simultaneously boosts the detection rate and reduces the complexity, while improving the accuracy of the resulting pose.


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Contact Person(s)

Tolga Birdal
M.Sc. Tolga Birdal

Working Group

Slobodan Ilic
Dr. Slobodan Ilic
Tolga Birdal
M.Sc. Tolga Birdal


Technische Universität München
Institut für Informatik / I16
Boltzmannstr. 3
85748 Garching bei München

Tel.: +49 89 289-17058
Fax: +49 89 289-17059
Visit our lab at Garching.

internal project page

Please contact Tolga Birdal for available student projects within this research project.

Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 21 Jun 2017 - 23:38 - TolgaBirdal

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