We have developed a method for recognizing and tracking known three-dimensional objects in scene images captured from a user’s viewpoint. The method uses multiple color histogra...
— We present an algorithm for learning 3D object models from partial object observations. The input to our algorithm is a sequence of 3D laser range scans. Models learned from th...
Michael Ruhnke, Bastian Steder, Giorgio Grisetti, ...
We propose a novel scheme for using supervised learning for function-based classification of objects in 3D images. During the learning process, a generic multi-level hierarchical ...
Recognizing 3D objects from arbitrary view points is one of the most fundamental problems in computer vision. A major challenge lies in the transition between the 3D geometry of o...
This paper addresses view-invariant object detection and pose estimation from a single image. While recent work focuses on object-centered representations of point-based object fe...