Robotics Ph.D. Speaking Qualifier

  • Remote Access - Zoom
  • Virtual Presentation
  • LEONID KESELMAN
  • Ph.D. Student
  • Robotics Institute
  • Carnegie Mellon University
Speaking Skills

Direct Fitting of Mixture Models

There exist many choices of 3D shape representation. Some recent work has advocated for the use of Gaussian Mixture Models as a compact representation for 3D shapes and scenes. These models are typically fit to point clouds, even when the shapes were obtained as 3D meshes. Here we present a formulation for fitting Gaussian Mixture Models (GMMs) directly to a triangular mesh instead of using points sampled from its surface. Part of this work analyzes a general formulation for evaluating likelihood of geometric objects in the limit of sampled points. These modifications enable fitting higher-quality GMMs under a wider range of initialization conditions. The resulting GMMs are shown to produce an improvement in 3D registration for both meshes and RGB-D frames.

Committee:
Martial Hebert (Advisor)
Christopher G. Atkeson
David Held
Achal Dave

Zoom Particiption. See announcement.

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