Annotating LiDAR Data for Autonomous Driving

About the project

3D LiDAR Point Cloud Bounding Box Annotation (Object Detection) Project Description: Overview: We are looking for experienced data annotators to perform high-quality 3D LiDAR bounding box labeling (cuboid annotation) on point cloud datasets for autonomous driving/robotics perception models. Scope of Work: Annotate Objects: Draw tight, precise 3D bounding boxes (cuboids) around target objects in point cloud scenes. Classes to Label: Vehicles (cars, trucks, buses), Pedestrians, Cyclists, and Miscellaneous obstacles. Orientation & Heading: Correctly align the orientation vector (yaw/heading direction) of each bounding box. Temporal Tracking (Interpolation): Track objects across sequential frames, ensuring consistent object IDs and smooth transitions. Strict Quality Control: Adhere to strict labeling guidelines regarding box tightness, minimum point count thresholds, and ground-level alignment. Required Skills & Experience: Proven experience with 3D Point Cloud / LiDAR annotation (not just 2D image labeling). Familiarity with standard annotation platforms (e.g., CVAT, Supervisely, Scale AI, LabelCloud, BAT, or similar tools). Strong spatial awareness and understanding of $X, Y, Z$ coordinate systems and pitch/yaw/roll rotations. High attention to detail—accuracy and consistency are critical for this project. Project Details: Dataset Format: .pcd or .bin files. Volume: Initial batch of [Insert number, e.g., 500] frames. Successful completion will lead to long-term, ongoing work. Tool: We will provide access to the labeling platform [Or specify: "Please specify which LiDAR annotation tools you are proficient in"]. How to Apply: To be considered, please reply with: A brief summary of your 3D LiDAR annotation experience. A screenshot or link to past 3D annotation work (if available). Your availability (hours per week) and your estimated rate per 100 frames. Note: Shortlisted candidates will be asked to complete a quick, 3-frame qualification test to demonstrate their labeling accuracy.

Skills required