2023
DOI: 10.1109/access.2022.3233411
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Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet

Abstract: Point clouds are generated by light imaging, detection and ranging (LIDAR) scanners or depth imaging cameras, which capture the geometry from the scanned objects with high accuracy. Unfortunately, these systems are unable to identify the semantics of the objects. Semantic 3D point clouds are an important basis for modeling the real world in digital applications. Manual semantic segmentation is a labor and cost intensive task. Automation of semantic segmentation using machine learning and deep learning (DL) app… Show more

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Cited by 5 publications
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“…Unlike 2D detection based on image data from optical sensors, LiDAR's spatial sensing capabilities are advantageous for object recognition in 3D spaces [5]. LiDAR is a radar system that detects a target's position, velocity, and other characteristic parameters by emitting a laser beam [6]. Compared to traditional two-dimensional remote sensing data, the point cloud contains geometric information and potentially encompasses information about color or intensity [7].…”
Section: Introductionmentioning
confidence: 99%
“…Unlike 2D detection based on image data from optical sensors, LiDAR's spatial sensing capabilities are advantageous for object recognition in 3D spaces [5]. LiDAR is a radar system that detects a target's position, velocity, and other characteristic parameters by emitting a laser beam [6]. Compared to traditional two-dimensional remote sensing data, the point cloud contains geometric information and potentially encompasses information about color or intensity [7].…”
Section: Introductionmentioning
confidence: 99%