3D LiDAR Point Cloud Annotation – Autonomous Vehicle Dataset (Anteater Series)
Scale AI3D SensorBounding BoxSegmentation
Worked on large-scale 3D LiDAR point cloud annotation for autonomous vehicle training datasets. Labeled vehicles, pedestrians, cyclists, road infrastructure, and environmental objects using 3D cuboids and spatial segmentation techniques. Ensured precise object boundary alignment across frames and maintained temporal consistency in multi-frame sequences. Followed strict annotation guidelines and quality control processes to meet accuracy benchmarks for production-level autonomous driving models. Contributed to improving object detection, tracking, and scene understanding performance for machine learning systems.
Worked on large-scale 3D LiDAR point cloud annotation for autonomous vehicle training datasets. Labeled vehicles, pedestrians, cyclists, road infrastructure, and environmental objects using 3D cuboids and spatial segmentation techniques. Ensured precise object boundary alignment across frames and maintained temporal consistency in multi-frame sequences. Followed strict annotation guidelines and quality control processes to meet accuracy benchmarks for production-level autonomous driving models. Contributed to improving object detection, tracking, and scene understanding performance for machine learning systems.
2024 - 2024