Title: Histograms of Gaussian normal distribution for feature matching in clutter scenes,
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.