3D shape representation, matching and recognition

Development of new methods for describing 3D shapes is an important topic in object recognition, model-based manipulation, and digital geometry processing. In the early days of computer vision, an object is usually modeled with global representations such as constructive solid geometry, generalized cylinders, or deformed superquadrics. Recently, more sophisticated representations such as shape distributions are developed, which allow for matching of objects under general similarity metrics. One drawback of such global schemes is that they are not suitable for matching with scenes where the target objects are only partially visible due to occlusion or limited view fields. In the talk, I will report our efforts in combining global and local representations to develop efficient methods for the partial object matching and analysis. The topics include a new shape representation scheme which uses a probabilistic bag-of-words model, a shape matching algorithm based on a dimension amnesic pyramid match kernel, and a shape space approach to animation of 3D human faces.