Abstract
Abstract. The extraction and description of keypoints as salient image parts has a long tradition within processing and analysis of 2D images. Nowadays, 3D data gains more and more importance. This paper discusses the benefits and limitations of keypoints for the task of fusing multiple 3D point clouds. For this goal, several combinations of 3D keypoint detectors and descriptors are tested. The experiments are based on 3D scenes with varying properties, including 3D scanner data as well as Kinect point clouds. The obtained results indicate that the specific method to extract and describe keypoints in 3D data has to be carefully chosen. In many cases the accuracy suffers from a too strong reduction of the available points to keypoints.
Highlights
The detection and description of keypoints is a well studied subject in the field of 2D image analysis
The three angular and the one distance value describe the relationship between the two points and the two normal vectors. These four values are added to the histogram of the point p, which shows the percentage of point pairs in the neighborhood of p, which have a similar relationship
A mean histogram μi is computed from all Point Feature Histograms (PFH) point histograms at radius ri
Summary
The detection and description of keypoints is a well studied subject in the field of 2D image analysis. Two dimensional keypoints have been used in many different applications from image registration and image stitching, to object recognition, to 3D reconstruction by structure from motion. Previous publications on the evaluation of different 3D keypoint detectors focus on shape retrieval (Bronstein et al, 2010) or 3D object recognition (Salti et al, 2011). These works show that keypoint detectors behave very differently in terms of execution time and repeatability of keypoint detection under noise and transformations. Different combinations of keypoint detectors and descriptors are compared with respect to the gain in accuracy of the final fusion results
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