![]() ![]() Standard LiDAR sensor output is a sequence of 3D point cloud frames, with a typical capture rate of 10 frames per second. ![]() ![]() This post, we focus specifically on labeling LiDAR data. In this series, we show you how to train an object detection model that runs on point cloud data to predict the location of vehicles in a 3D scene. The growing availability of LiDAR sensors has increased interest in point cloud data for machine learning (ML) tasks, like 3D object detection and tracking, 3D segmentation, 3D object synthesis and reconstruction, and using 3D data to validate 2D depth estimation. Some new mobile devices even include LiDAR sensors. Autonomous vehicle companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles.Īs LiDAR sensors become more accessible and cost-effective, customers are increasingly using point cloud data in new spaces like robotics, signal mapping, and augmented reality. LiDAR (light detection and ranging) is a method for determining ranges by targeting an object or surface with a laser and measuring the time for the reflected light to return to the receiver. In part 2, we walk through how to train a model on your dataset and deploy it to production. In part 1, we discuss the dataset we’re using, as well as any preprocessing steps, to understand and label data. In this two-part series, we demonstrate how to label and train models for 3D object detection tasks. ![]()
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