We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure" model. The 3D shape of an object class is represented by sets...
Kristen Grauman, Gregory Shakhnarovich, Trevor Dar...
We present the latest developments in modeling 3D biomedical data via the Medial Scaffold (MS), a 3D acyclic oriented graph representation of the Medial Axis (MA) [LK07, SP08]. Th...
Frederic F. Leymarie, Ming-Ching Chang, Celina Imi...
The automatic reconstruction of 3D models from image sequences is still a very active field of research. All existing methods are designed for a given camera model, and a new (and...
We present a system for fast model-based segmentation and 3D pose
estimation of specular objects using appearance based specular
features. We use observed (a) specular reflection...