Octree-based region growing for point cloud segmentation

By A. Vo, L. Truong-Hong, D. Laefer, M. Bertolotto. 2015

655 citations recorded by the source corpus.

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Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020, background): “models, the local density of points is related to the setting of voxel sizes [102].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “Additionally, all of the region based methods require extensive computation resources, taxing both time and memory [29,48].”
Estimating Individual Tree Height and Diameter at Breast Height (DBH) from Terrestrial Laser Scanning (TLS) Data at Plot Level (2018, background): “minimum pixel size criteria are reached [55].”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “Inspired from the techniques used in the 2D/image domain, several methods ranging from region growing (e.g., Vo et al. [2015]) and graph-based approaches (e.g., Sima and Nuchter [2013]) to superpixels (e.g., Papon et al. [2013] and Aijazi et al. [2013]) have been considered for segmenting 3D data.”