Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications

By Dimitris Zermas, I. Izzat, N. Papanikolopoulos. IEEE International Conference on Robotics and Automation, 2017

355 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems (2020, background): “[23] processed the range image row by row.”
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving (2019, background): “Point cloud segmentation has been studied in [4][10][23].”
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud (2018, methodology): “Non-deep-learning methods [1], [7], [8] usually involve several stages such as ground removal, clustering, and classification.”
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud (2017, methodology): “This paradigm, despite its popularity [2], [3], [4], [5], has several disadvantages: a) Ground segmentation in the above pipeline usually relies on hand-crafted features or decision rules – some approaches rely on a scalar threshold [6] and others require more complicated features such as surface no”
A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles (2018, methodology): “In order to highlight the performance of the proposed algorithm, the comparison between the various segmentation results of the proposed method and the other two methods described in [5] and [6] are shown in Fig 8.”