{'fraction_non_alphanumeric': 0.013727560718057022, 'fraction_numerical': 0.006335797254487857, 'mean_word_length': 5.076923076923077, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Performance Analysis of Computer Vision Based Feature Detectors for 3D Laser Rasterized Images

The 3D point cloud processing and scan registration is a computationally expensive task as each scan contains thousands of points. An effective solution is to use feature detection methods to reduce the dimensions of the point cloud. These methods include feature detection directly from a point cloud or using a 2D projection. The 2D projection methods have proven to require lower computational resources. In this paper, a pinhole camera model is used to rasterize the 3D point cloud to a 2D image. This paper provides a detailed analysis of the performance of several computer vision-based feature detectors on rasterized laser images. We perform an extensive evaluation on multiple datasets including, KITTI, FORD campus, and an indoor dataset. The primary goal is to compare the quality of features and computational requirements for each method.