From Acquisition to Understanding of 3D Shapes

Yangyan Li and Matthias Niessner

(Stanford University)

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Date: April 8, 2015


Understanding 3D shapes is an essential but very challenging task for many scenarios ranging from robotics to computer graphics and vision applications. In particular, range sensing technology has made the shape understanding problem even more relevant, as we can now easily capture the geometry of the real world. In this talk, we will demonstrate how we can obtain a 3D reconstruction of an environment, and how we can exploit these results to infer semantic attributes in a scene. More specifically, we introduce a SLAM technique for large-scale 3D reconstruction using a Microsoft Kinect sensor. We will then present a method to jointly segment and classify the underlying 3D geometry. Furthermore, we propose to locate and recognize individual 3D shapes during the scanning, where large shape collections are used as the source of prior knowledge. In the future, we plan to extend our work from reconstructing geometry and object labelling to inferring objects’ physical properties such as weights.

Further Information:

Yangyan Li is a post-doctoral scholar at Prof. Leonidas J. Guibas’ Geometric Computation Group in Stanford University, affiliated with Max Planck Center for Visual Computing and Communication. Yangyan received his PhD degree rom Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, under the supervision of Prof. Baoquan Chen in 2013. His primary research interests fall in the field of Computer Graphics with an emphasis on 3D reconstruction.

Matthias Niessner is a visiting assistant professor at Stanford University affiliated with the Max Planck Center for Visual Computing and Communication. Previous to his appointment at Stanford, he earned his PhD from the University of Erlangen-Nuremberg, Germany under the supervision of Günther Greiner. His research focuses on different fields of computer graphics and computer vision, including real-time rendering, reconstruction of 3D scene environments, and semantic scene understanding.

Created: Thursday, April 9th, 2015