A novel local surface feature for 3D object recognition under clutter and occlusion
This paper presents a highly distinctive local surface feature called the TriSI feature for recognizing 3D objects in the presence of clutter and occlusion. For a feature point, we first construct a unique and repeatable Local Reference Frame (LRF) using the implicit geometrical information of neighboring triangular faces. We then generate three signatures from the three orthogonal coordinate axes of the LRF. These signatures are concatenated and then compressed into a TriSI feature. Finally, we propose an effective 3D object recognition algorithm based on hierarchical feature matching. We tested our TriSI feature on two popular datasets. Rigorous experimental results show that the TriSI feature was highly descriptive and outperformed existing algorithms under all levels of Gaussian noise, Laplacian noise, shot noise, varying mesh resolutions, occlusion, and clutter. Moreover, we tested our TriSI-based 3D object recognition algorithm on four standard datasets. The experimental results show that our algorithm achieved the best overall recognition results on these datasets.
local surface feature 3d object recognition clutter occlusion point-cloud feature description