Abstract
Three-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface descriptor using point-pair transformation feature histograms (PPTFHs) to address these challenges. The generation process of the PPTFH descriptor consists of three steps. First, a simple but efficient strategy is introduced to partition the point-pair sets on the local surface into four subsets. Then, three feature histograms corresponding to each point-pair subset are generated by the point-pair transformation features, which are computed using the proposed Darboux frame. Finally, all the feature histograms of the four subsets are concatenated into a vector to generate the overall PPTFH descriptor. The performance of the PPTFH descriptor is evaluated on several popular benchmark datasets, and the results demonstrate that the PPTFH descriptor achieves superior performance in terms of descriptiveness and robustness compared with state-of-the-art algorithms. The benefits of the PPTFH descriptor for 3D surface matching are demonstrated by the results obtained from five benchmark datasets.
Highlights
Four popular datasets were selected to conduct a series of experiments: the Bologna retrieval (BR) dataset [24] for 3D shape retrieval, the Stanford 3D scanning repository (SDSR) dataset [30] for partial 3D data registration, the UWA dataset [31] for 3D object recognition, and the Kinect dataset [32] for 3D object recognition with low-quality surfaces
Another shows that the most current PPF-based methods outperform recent local reference frame (LRF)-based methods in term of descriptive power, and it can be explained that the most current PPF-based descriptors make full use of the spatial and geometric cues caused by the point-pair set partition strategy and novel point-pair features, and they are not affected by unstable LRFs
We proposed a novel point-pair transformation feature histograms (PPTFHs) descriptor for 3D surface description, together with a proposed point-pair division strategy
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. A modified version of the PFH was proposed in [20]; that is, the fast PFH (FPFH) descriptor which is constructed by weighting the simplified PFH associated with all neighbor points These descriptors only make use of single geometric or spatial features to encode surface information, resulting in poor performance in terms of descriptiveness under the effect of various disturbances (noise, clutter, occlusion, mesh decimation, etc.). Based on this analysis, existing descriptors either only extract single geometric and spatial information or include unstable geometric and spatial information encoded by the LRFs, resulting in low descriptiveness and weak robustness under the influence of disturbances [23] To address these drawbacks, we propose a novel local descriptor using point-pair transformation feature histograms (PPTFHs) for discriminative and robust surface description.
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