English

Abstract

In the present paper, we have proposed a high-performance 3D recognition method based on the reference point ensemble, which is a natural extension of the generalized Hough transform. The reference point ensemble consists of several reference points, each of which is color-coded by green or red, where the red reference points are used to verify the hypothesis, and the green reference points are used for Hough voting. The configuration of the reference points in the reference point ensemble is designed depending on the model shape. In the proposed method, a set of reference point ensembles is generated by the local features of a given 3D scene. Each generated reference point ensemble is a hypothetical 3D pose of a given object in the scene. Hypotheses passing through the verification by the red reference points are used for Hough voting. Hough voting is performed independently in each green point space, which reduces the voting space to three dimensions. Although a six-dimensional voting space is generally needed for 3D recognition, in the proposed method, the six-dimensional voting space is decomposed into a few three-dimensional spaces. This decomposition and the verification using green or red reference points have been demonstrated experimentally to be effective for 3D recognition. In other words, the effective recognition has been achieved by skillfully switching the following two different modes. (A) Individual mode: Voting of the hypothesis independently in each green Hough space and verifying of hypothesis with red reference points are done in this mode. (B) Ensemble mode : Verifying of registration into PHL(promising hypothesis list) and aggregating of total votes are done in this mode. This mode switching mechanism is the most significant characteristic of the proposed method.