Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

TL;DR

This paper introduces Rotational Projection Statistics (RoPS), a novel 3D local surface descriptor and recognition method that is robust to noise, occlusion, and varying mesh resolution, achieving high recognition accuracy on multiple datasets.

Contribution

The paper proposes a new local reference frame and RoPS feature descriptor for 3D object recognition, demonstrating superior robustness and accuracy over existing methods.

Findings

Achieved recognition rates of 100% on Bologna dataset

Achieved recognition rates of 98.9% on UWA dataset

Demonstrated robustness to noise and mesh resolution variations

Abstract

Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: Local Reference Frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and 96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari Venezia Datasets.