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🔷 3D LiDAR Cuboid Annotation

High-precision 3D cuboid annotation for LiDAR point cloud datasets used in autonomous driving, robotics, and spatial AI applications. Objects are accurately bounded in 3D space with consistent orientation, dimension alignment, and temporal tracking across frames. Our workflow follows client-defined class schemas, calibration settings, and multi-stage QA validation to ensure spatial accuracy and annotation consistency.

🧩 3D LiDAR Semantic Segmentation

Pixel-level (point-level) semantic labeling of LiDAR point clouds to classify environmental elements such as vehicles, pedestrians, roads, lanes, vegetation, and infrastructure. We deliver structured, high-quality segmentation using detailed guidelines, trained annotators, and layered QA processes to support model training for perception and scene understanding systems.

🗺️ 3D LiDAR Mapping / Scene Structuring

Structured annotation and organization of LiDAR scenes to support environment mapping, object localization, and spatial analysis. This includes class labeling, spatial grouping, and scene-level organization to improve dataset usability for machine learning and simulation workflows. Projects are delivered with consistency checks, metadata alignment, and quality-controlled outputs.