Proof of Work, Not Promises
Real projects. Real data. Real results. From 3D LiDAR point clouds to pixel-level segmentation — here’s what production-grade annotation looks like.
3D LiDAR Point Cloud Annotation — KITTI Format Cuboids with Sensor Fusion
5,000 KITTI frames annotated with 3D cuboids and camera context images. Full sensor fusion pipeline using velodyne_points + image_00 data. Six object classes across autonomous driving scenarios. Exported in KITTI, Datumaro 3D, and CVAT JSON formats.
From Our Portfolio
Bounding Box Detection
Bounding Box — Multi-Domain Object Detection
25,000+ bounding box annotations across pedestrians, vehicles, and aerial views. Multi-domain coverage spanning 6 clients and internal pilots.
View →Polygon Segmentation
Polygon Segmentation — Industrial & Automotive Instance Seg
1,000 images with pixel-precise polygon instance segmentation for industrial and automotive applications. Subcontracted under NDA.
View →Semantic Segmentation
Semantic Segmentation — Full-Scene Parsing for Autonomous Nav
Pixel-level scene parsing for autonomous navigation. Every pixel classified into road, sidewalk, vehicles, pedestrians, sky, and infrastructure.
View →Skeleton Keypoints
Skeleton Keypoints — 17-Point COCO Human Pose Estimation
17-point COCO-format human pose annotation across diverse scenes. Keypoint visibility flags, occlusion handling, and multi-person tracking.
View →Polyline Lane Detection
Polyline — Lane Detection & Linear Feature Mapping
Lane boundary and road marking annotation using polylines. Supports HD map generation and autonomous navigation lane-keeping systems.
View →Want Results Like These?
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