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A GPU-Accelerated Framework for Simulating LiDAR Scanning
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AI
DOI：10.1109/TGRS.2022.3165746.png)
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摘要
En 中文
In this work, we present an efficient graphics processing unit (GPU)-based light detection and ranging (LiDAR) scanner simulator. Laser-based scanning is a useful tool for applications ranging from reverse engineering or quality control at an object scale to large-scale environmental monitoring or topographic mapping. Beyond that, other specific applications require a large amount of LiDAR data during development, such as autonomous driving. Unfortunately, it is not easy to get a sufficient amount of ground-truth data due to time constraints and available resources. However, LiDAR simulation can generate classified data at a reduced cost. We propose a parameterized LiDAR to emulate a wide range of sensor models from airborne to terrestrial scanning. OpenGL's compute shaders are used to massively generate beams emitted by the virtual LiDAR sensors and solve their collision with the surrounding environment even with multiple returns. Our work is mainly intended for the rapid generation of datasets for neural networks, consisting of hundreds of millions of points. The conducted tests show that the proposed approach outperforms a sequential LiDAR scanning. Its capabilities for generating huge labeled datasets have also been shown to improve previous studies.
Keyword:
Laser radar
Sensors
Solid modeling
Point cloud compression
Laser beams
Three-dimensional displays
Semantics
General-purpose computing on graphics processing units (GPUs)
light detection and ranging (LiDAR) simulation
point cloud
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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引用论文
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