Performance Evaluation of 3D Keypoint Detectors and Descriptors on Coloured Point Clouds in Subsea Environments
This work addresses the lack of comprehensive surveys for subsea 3D keypoint detection, which is important for researchers and practitioners in underwater robotics and mapping, but it is incremental as it builds on existing methods in a new domain.
The paper tackled the problem of identifying the best 3D keypoint detector and descriptor combinations for subsea environments by evaluating them on a challenging field dataset, and proposed a novel method of fusing images with underwater laser scans to create colored point clouds for studying 6D descriptors.
The recent development of high-precision subsea optical scanners allows for 3D keypoint detectors and feature descriptors to be leveraged on point cloud scans from subsea environments. However, the literature lacks a comprehensive survey to identify the best combination of detectors and descriptors to be used in these challenging and novel environments. This paper aims to identify the best detector/descriptor pair using a challenging field dataset collected using a commercial underwater laser scanner. Furthermore, studies have shown that incorporating texture information to extend geometric features adds robustness to feature matching on synthetic datasets. This paper also proposes a novel method of fusing images with underwater laser scans to produce coloured point clouds, which are used to study the effectiveness of 6D point cloud descriptors.