Recognizing Objects in Range Data Using Regional Point Descriptors

Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas Bülow, Jitendra Malik
• Keywords: Cluttered Scenes, Computer Vision, Context Based Descriptors, ECCV 2004, Harmonic Shape Contexts, Regional Shape Descriptors