Yongtao Yu

TL;DR: Wang et al. as mentioned in this paper proposed a curb-based method for road surface extraction from mobile laser scanning (MLS) point clouds, which first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation difference thresholds.

TL;DR: The results show that road surfaces are correctly segmented, and street light poles are robustly extracted with a completeness exceeding 99%, a correctness exceeding 97%, and a quality exceeding 96%, thereby demonstrating the efficiency and feasibility of the proposed algorithm to segment road surfaces and extract street light pole from huge volumes of mobile LiDAR point-clouds.

TL;DR: This review presents a more in-depth description of current mobile LiDAR studies on road information inventory, including the detection and extraction of road surfaces, small structures on the road surfaces and pole-like objects.

TL;DR: Comparative experiments demonstrate that the uses of waveform representation and deep Boltzmann machines contribute to the improvement of classification accuracies of tree species.

TL;DR: This paper presents a novel method for automated extraction of road markings directly from three dimensional point clouds acquired by a mobile light detection and ranging (LiDAR) system that achieves better performance and accuracy than those of the two existing methods.