Annotating LiDAR Data for Autonomous Driving
Budget: $250 – $750 USD
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.
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.
Related categories:
3D Rendering
3D Modelling
3D Animation
OpenGL
Computer Vision
3D Visualization
Object Detection
AI Development