Annotation Techniques

Industry-leading methodologies engineered for precision, consistency, and scale across every data modality — from 2D imagery to 3D point clouds.

Technique 01

Bounding Boxes (2D & 3D)

Rectangular and cuboid object localisation for detection and depth estimation tasks. The foundational annotation technique — applied at scale across millions of frames for computer vision models in automotive, retail, and security applications.

2D Bounding Boxes Axis-aligned rectangles for object detection across image and video datasets.
3D Cuboids Six-DOF cuboids for depth-aware localisation in LiDAR and stereo camera data.
Rotated Boxes Oriented bounding boxes for aerial and text detection use cases.
Attribute Tagging Class, confidence, occlusion, truncation, and custom metadata per annotation.
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Technique 02

Semantic Segmentation

Pixel-perfect class labelling for comprehensive scene understanding. Every pixel assigned a semantic class — enabling models to understand scene context rather than simply detecting objects.

Semantic Segmentation Assign class labels to every pixel in an image for full scene understanding.
Instance Segmentation Distinguish individual object instances of the same class with unique masks.
Panoptic Segmentation Combines semantic and instance segmentation for comprehensive scene parsing.
Medical Masks Organ and lesion boundary masks for diagnostic imaging AI pipelines.
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Technique 03

Keypoint Annotations

Structural landmark identification for pose estimation, facial recognition, and body analysis models. Each keypoint precisely marks anatomically significant positions with occlusion flags and confidence scores.

Human Pose Estimation 17-point COCO skeleton and custom joint schemas for action recognition.
Facial Landmarks 68 to 478-point facial mesh for expression, gaze, and identity models.
Hand & Finger Tracking 21-point hand skeleton for gesture control and sign language AI.
Animal Pose Custom keypoint schemas for veterinary AI and wildlife monitoring.
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Technique 04

LiDAR & RADAR Annotation

3D point cloud labelling for autonomous vehicle and robotics applications. Our teams annotate dense LiDAR scans with semantic class, instance ID, and geometric attributes for the most demanding AV programs.

Point Cloud Segmentation Per-point class labelling across 64 to 128-line LiDAR sensors.
3D Object Detection Cuboid annotation with heading, velocity, and object class in point clouds.
RADAR Annotation Range-Doppler map labelling for velocity estimation and ADAS models.
Temporal Tracking Consistent object IDs across point cloud frames for motion modelling.
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Technique 05

Polygons & Polylines

Precise boundary delineation for irregular shapes and lane marking datasets. Polygon tools capture complex object outlines with sub-pixel accuracy, while polylines define roads, fences, and structural boundaries.

Polygon Annotation Multi-vertex outlines for irregular objects — buildings, foliage, vehicles, bodies of water.
Lane Marking Polylines Dashed, solid, double, and curved lane annotation for ADAS training.
Road Edge & Curb Road boundary polylines for HD map generation and path planning.
Spline Curves Smooth continuous curve annotation for organic shapes and vehicle contours.
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Technique 06

Multisensor Fusion

Synchronised annotation across camera, radar, and LiDAR sensor streams. The most advanced annotation challenge — requiring precise temporal alignment, cross-sensor consistency, and expert understanding of sensor physics.

Camera-LiDAR Fusion Project and align point clouds with camera images for joint annotation.
Temporal Sync Frame-accurate synchronisation across sensors with different frame rates.
Cross-Sensor Consistency Unified object IDs and class labels maintained across all sensor modalities.
4D Annotation 3D spatial + temporal tracking for full scene understanding over time.
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At a Glance

Technique Comparison

Technique Best For Modality Complexity Typical Use Case
Bounding Boxes Object detection & counting Image, Video Low–Med Product detection, traffic monitoring
Semantic Segmentation Scene understanding Image, Video, Medical High Autonomous driving, medical imaging
Keypoint Annotation Pose & structure recognition Image, Video Medium Human pose, facial AI, sports analytics
LiDAR / RADAR 3D depth & spatial understanding 3D Point Cloud Very High Autonomous vehicles, robotics
Polygons & Polylines Precise boundary delineation Image, Aerial, Video Medium–High Lane detection, property mapping
Multisensor Fusion Full environment modelling Multi-sensor Highest Level 4–5 autonomous driving
Precision at Scale

Choose the Right Technique

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