The Taub Faculty of Computer Science Events and Talks

We presents a novel approach to pose estimation and model-based recognition of specular objects in difficult viewing conditions, such as low illumination, cluttered background, and large highlights and shadows that appear on the object of interest. In such challenging conditions conventional features are unreliable. We show that under the assumption of a dominant light source, specular highlights produced by a known object can be used to establish correspondence between its image and the 3D model, and to verify the hypothesized pose and the identity of the object.

Previous methods that use highlights for recognition make limiting assumptions such as known pose, scene-dependent calibration, simple shape, etc. The proposed method can efficiently recognize free form specular objects in arbitrary pose and under unknown lighting direction. It uses only a single image of the object as its input and outputs object identity, full pose, and the lighting direction in the scene.

This is a joint work with Aaron Netz