Exploiting Model Similarity for Indexing and Matching to a Large Model Database

Abstract

This paper proposes a novel method to exploit model similarity in model-based 3D object recognition. The scenario consists of a large 3D model database of vehicles, and rapid indexing and matching needs to be done without sequential model alignment. In this scenario, the competition amongst shape features from similar models may pose serious challenge to recognition. To solve the problem, we propose to use a metric to quantitatively measure model similarities. For each model, we use similarity measures to define a model-centric class (MCC), which contains a group of similar models and the pose transformations between the model and its class members. Similarity information embedded in a MCC is used to boost matching hypotheses generation so that the correct model gains more opportunities to be hypothesized and identified. The algorithm is implemented and extensively tested on 1100 real LADAR scans of vehicles with a model database containing over 360 models.