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. See the 3D Velodyne point clouds in KITTI's dataset for example.Autonomous vehicle companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. This version of the app assumes the LiDAR data to be stored in a binary float matrix (.bin extension).Įach column is a point, where the rows are in the following order: x, y, z, and intensity (little endian). Its corresponding table row should also be deleted. Then press the delete or backspace key to delete the bounding box. You can also delete a bounding box by selecting its corresponding row, and the bounding box should turn blue.To change label, just change the value in the dropdown input, and the bounding box's object id will save automatically on input change.Click on the index of a bounding box in the 'object id table' and its corresponding bounding box will change color to blue.When a bounding box is selected, the input for its corresponding row in the object ID table is focused.To delete bounding box, press the backspace/delete key while the bounding box is selected.Then click somewhere strictly inside a bounding box, and the borders will turn blue. You can tell it is hovered if it turns red. To 'select' bounding box, first hover above it.To rotate bounding box, click and drag the point that is not a corner vertex (it should be between two corner vertices) and box will rotate with the point.It will turn blue if your mouse is close enough to it. You can only click and drag on a corner vertex if it is blue.To resize bounding box, click and drag the 'corner' vertices.Click and drag to draw a bounding box and release to set it.(Note: While in '2D move' mode, hold the control key to be in 'draw mode') You can also adjust the height of the 3D bounding box with ctrl down and click and drag the center point.You can label objects in '3D' mode (see 'labelling bounding boxes').Left click and drag to orbit around the point cloud.To export your bounding boxes, click on the 'save' button to the top-left.See 'Controls' section to learn more about user interface.

To draw a bounding box, starting recording first and then simply hold mouse down and drag across the screen with your ctrl down.To launch the app, use python to run the app.py in app/ folder.3D LiDAR Annotator forked from bernwang/LiDAR-annotator

Lidar annotator