PhD Thesis

Continuum Machine Learning for Object Perception

Work default illustration


  • Started: 01/04/2019
  • Thesis project read: 18/02/2020


6D model-based rigid object pose retrieval from vision is an important problem in computer vision. Early solutions address it as an instance recognition problem, by means of template matching approaches. In contrast, in this work we introduce a learning-based approach. To this end, the projection space is defined by using an own-developed descriptor that it is then optimally segmented. After that, we make use of a probabilistic model to match every given instance to its belonging segment. Thus, obtaining a valid subspace for its dominion. The method only requires RGB information as input, as well as a full CAD model of the observed objects. It intends to be adequate for texture-less objects. Robustness to occlusion and clutter are desirable attributes for the method, also in the scope of the project.

The work is under the scope of the following projects:

  • HuMoUR: Markerless 3D human motion understanding for adaptive robot behavior (web)
  • R3OBJ: Reconstrucción 3D, localización y segmentación automática de objetos a partir de imágenes (web)