City-scale ground segmentation of aerial LiDAR by image-analogous gradients

Muhammad Azam; Derek Jacoby; Yvonne Coady

doi:10.1117/12.2680316

7 June 2023 City-scale ground segmentation of aerial LiDAR by image-analogous gradients

Muhammad Azam, Derek Jacoby, Yvonne Coady

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127010F (2023) https://doi.org/10.1117/12.2680316
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

LiDAR Point Cloud segmentation is a key input to downstream tasks such as object recognition and classification, obstacle avoidance, and even 3D reconstruction. Akey challenge in the segmentation of large city-scale datasets is uneven distribution of points to specific classes and significant class imbalances. As highly detailed point cloud datasets of urban environments become available, neural networks have shown significant performance in recognizing large well-defined objects. However, data is fed into these networks in chunks and the scheme by which data is presented for training and evaluation can have a significant impact on performance. In this work, we establish a method analogous to gradients in image processing to segment the ground in point clouds, achieving an accuracy of 91.4% on the Sensaturban dataset. By isolating the ground, we reduce the quantity of classes that need to be segmented from structures in urban LiDAR and improve data partitioning schemes when combined with random/grid down-sampling techniques for neural network inputs.

Citation Download Citation

Muhammad Azam, Derek Jacoby, and Yvonne Coady "City-scale ground segmentation of aerial LiDAR by image-analogous gradients", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127010F (7 June 2023); https://doi.org/10.1117/12.2680316

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KEYWORDS

Point clouds

LIDAR

Image segmentation

Image processing

Education and training

Autonomous vehicles

Neural networks

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