3D LiDAR and Bounding Box Annotations

LiDAR and point cloud datasets require 3D annotations that capture an object’s center location, dimensions, and orientation, making them important for applications such as autonomous driving, robotics, mapping, and spatial AI. The tutorial explains how FiftyOne’s open-source platform represents 3D detections using the same Detection label type as 2D data but with location, rotation, and dimensions instead of a 2D bounding box. It demonstrates loading a KITTI-based point-cloud dataset, adding and modifying cuboid bounding boxes, and using rotations in radians after first aligning a box’s position and size. It also covers 3D polylines for shapes such as lanes, defined through coordinate-based vertices, and orthographic projections that create bird’s-eye-view images for easier browsing and dataset curation. The article notes that irregular, occluded, or sparsely sampled objects make annotation more difficult, requiring informed estimates of their geometry, and presents FiftyOne’s visualization and curation tools as support for refining annotations, validating data, and developing scalable 3D perception workflows.

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