Histograms of Gaussian normal distribution for feature matching in clutter scenes

TL;DR

This paper introduces HGND, a novel local feature descriptor based on Gaussian normal distribution histograms, which improves 3D object recognition accuracy in cluttered scenes by reducing feature mismatches.

Contribution

The paper proposes a new feature descriptor, HGND, utilizing local reference frames and Gaussian normal distribution histograms to enhance matching in cluttered 3D scenes.

Findings

HGND achieves higher matching rates than existing methods.

The approach is robust across diverse cluttered scenes.

Experimental results validate the effectiveness of HGND.

Abstract

3D feature descriptor provide information between corresponding models and scenes. 3D objection recognition in cluttered scenes, however, remains a largely unsolved problem. Practical applications impose several challenges which are not fully addressed by existing methods. Especially in cluttered scenes there are many feature mismatches between scenes and models. We therefore propose Histograms of Gaussian Normal Distribution (HGND) for extracting salient features on a local reference frame (LRF) that enables us to solve this problem. We propose a LRF on each local surface patches using the scatter matrix's eigenvectors. Then the HGND information of each salient point is calculated on the LRF, for which we use both the mesh and point data of the depth image. Experiments on 45 cluttered scenes of the Bologna Dataset and 50 cluttered scenes of the UWA Dataset are made to evaluate the robustness and descriptiveness of our HGND. Experiments carried out by us demonstrate that HGND obtains a more reliable matching rate than state-of-the-art approaches in cluttered situations.