![]() We first walk through a few approaches to reduce dataset size for labeling point clouds: tiling, fixed step sample, and voxel mean. For more information about the data types allowed in a Ground Truth point cloud, see Accepted Raw 3D Data Formats. In this case, the point cloud data is in xyzrgb format, an accepted format for a Ground Truth point cloud. The app allows you to use the built-in LiDAR scanners on mobile devices to scan a given area and export a point cloud file. The data we use in this post is a scan of an apartment building rooftop generated using the 3D Scanner App on an iPhone12 Pro. ![]() To accomplish this, we use Ground Truth and Amazon SageMaker notebook instances to perform labeling and all preprocessing and postprocessing steps. In this post, we walk through how to perform downsampling techniques to prepare your point cloud data for labeling, then demonstrate how to upsample your output labels to apply to your original full-size dataset using some in-sample inference with a simple ML model. For other modalities, like semantic segmentation, in which each point has its own label, you can use your downsampled labels to predict the labels on each point in the original point cloud, allowing you to perform a tradeoff between labeler cost (and therefore amount of labeled data) and a small amount of misclassifications of points in the full-size point cloud. When annotating downsampled point clouds, you can use the output 3D cuboids for object tracking and object detection tasks directly for training or validation on the full-size point cloud with little to no impact on model performance while saving labeling time. ![]() Like in the signal processing domain, point cloud downsampling approaches attempt to remove points while preserving the fidelity of the original point cloud. We refer to these approaches broadly as downsampling, similar to downsampling in the signal processing domain. Although large numbers of points may be renderable in a labeler’s workstation, labeler throughput can be reduced due to rendering time when dealing with multi-million sized point clouds, greatly increasing labeling costs and reducing efficiency.Ī way to reduce these costs and time is to convert point cloud labeling jobs into smaller, more easily rendered tasks that preserve most of the point cloud’s original information for annotation. Some new mobile devices even include LiDAR sensors, one of which supplied the data for this post! The growing availability of LiDAR sensors has increased interest in point cloud data for ML tasks, like 3D object detection and tracking, 3D segmentation, 3D object synthesis and reconstruction, and even using 3D data to validate 2D depth estimation.Īlthough dense point cloud data is rich in information (over 1 million point clouds), it’s challenging to label because labeling workstations often have limited memory, and graphics capabilities and annotators tend to be geographically distributed, which can increase latency. Ground Truth also supports sensor fusion of camera and LiDAR data with up to eight video camera inputs.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. ![]() Amazon SageMaker Ground Truth makes it easy to label objects in a single 3D frame or across a sequence of 3D point cloud frames for building machine learning (ML) training datasets. The LiDAR sensor output is a sequence of 3D point cloud frames (the typical capture rate is 10 frames per second). For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment. Autonomous vehicle companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles.
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