A Repeatable and Efficient Canonical Reference for Surface Matching

Alioscia Petrelli, Luigi Di Stefano

Abstract: The paper investigates on canonical references used for local surface description and matching. We formulate a novel proposal and carry out an extensive experimental evaluation addressing two major surface matching scenarios, namely shape registration and object recognition. We provide also a methodological contribution as, unlike previous work in the field, we propose a repeatability metric that captures the actual impact of the adopted local reference frame algorithm within surface matching tasks based on local 3D descriptors. Our proposal outperforms existing algorithms by a wide margin on several datasets acquired with different devices, such as laser scanners, stereo cameras and the Kinect, and in experiments relying on randomly extracted feature as well as state-of-the art key points.