THE CHALLENGE
3D Data Tells You Where Things Are
LiDAR captures depth, distance, and the shape of physical spaces, but raw point clouds don't tell a model what each point represents.
Annotation adds meaning to that data, helping AI recognize objects, surfaces, structures, and their relationships within a 3D environment.
Understand Objects in 3D
Identify objects with clear boundaries, dimensions, orientation, and precise position within the surrounding point cloud.
Capture Spatial Relationships
Help models understand depth, distance, orientation, and how objects relate to the surrounding space and environment.
Build Better 3D Training Data
Create consistent datasets for perception, mapping, robotics, automation, and other applications that rely on spatial information.
LIDAR ANNOTATION CAPABILITIES
Annotate 3D Data Around What Your Model Needs to Understand
Different applications need different levels of 3D detail. We build annotation workflows around the objects, surfaces, structures, and spatial information your project needs
3D Bounding Box Annotation
Place precise 3D boxes around objects to capture their location, dimensions, and orientation.
3D Cuboid Annotation
Create oriented cuboids for vehicles, people, machines, structures, and other objects within point clouds.
Point Cloud Segmentation
Separate points into objects, classes, or regions for more detailed 3D understanding.
Semantic Segmentation
Classify individual points by categories such as roads, vehicles, buildings, vegetation, or pedestrians.
Instance Segmentation
Separate individual objects even when multiple objects belong to the same category.
Point-Level Annotation
Label individual points when your application requires detailed information about specific parts of a scene.
3D Keypoint Annotation
Identify important points on objects or structures for pose, geometry, and spatial analysis.
Motion & Tracking Annotation
Track objects across sequential point clouds to capture movement and changes in position.
Custom LiDAR Annotation
Specialized workflows for the objects, environments, and requirements unique to your application.
3D ANNOTATION DEPTH
Capture the Level of Detail Your Data Requires
Not every 3D project needs the same level of detail. Annotation can range from individual objects to individual points, depending on what your model needs to understand.
The goal isn't to label every point. It's to capture the 3D information your model actually needs.
LIDAR ANNOTATION PROCESS
From Raw Point Clouds to Training-Ready Data
Every project starts by defining what the data needs to represent and ends with a validated dataset ready for your AI, mapping, robotics, or ML workflow.
AI + HUMAN ANNOTATION
Use AI for Speed. Use People for Spatial Judgment.
Automation can speed up repetitive 3D annotation, but sparse points, overlapping objects, and complex scenes still require people who can make careful spatial decisions.
AI-Assisted Annotation
Use automated tools to speed up repetitive 3D labeling across large point-cloud datasets.
Automated Object Detection
Use existing models to identify common objects and accelerate the initial annotation process.
Expert Human Review
Review difficult scenes, sparse point clouds, occlusions, and ambiguous objects.
Automated Validation
Check for missing labels, incorrect classes, inconsistent boundaries, and other potential errors.
LIDAR ANNOTATION QUALITY
3D Accuracy Has to Hold From Point to Point
Point clouds can contain thousands or millions of points, so small errors can change how a model understands an object's size, position, shape, or surroundings.
Clear Annotation Guidelines
Define consistent rules for object classes, boundaries, orientation, and point-level labeling.
Point-Level Quality Checks
Identify incorrect, missing, or inconsistent labels across dense and sparse areas.
3D Boundary Validation
Check object boundaries, dimensions, orientation, and placement within the point cloud.
Frame-to-Frame Consistency
Check that tracked objects remain consistent across sequential scans.
Human-in-the-Loop Review
Review difficult scenes and ambiguous annotations that require human judgment.
Edge-Case Analysis
Review occlusion, sparse points, reflections, overlapping objects, and complex environments.
DOMAIN & SCALE
LiDAR Annotation Built Around Your Environment
LiDAR data changes significantly by environment. A road, warehouse, factory, mapping project, or city scene can require very different annotation decisions.
WHY ANOTAG
Built Around the Way 3D AI Works
Our approach combines 3D annotation experience, automation, human review, and quality checks to create datasets that remain consistent across complex spatial environments.
3D Annotation Expertise
Workflows built around point clouds, depth, spatial relationships, and 3D objects.
Human + AI Workflows
Combine automation with human judgment when spatial context matters.
Spatial Accuracy
Maintain consistent object boundaries, positions, dimensions, and orientations.
Multi-Layer Quality
Check annotations at point, object, scene, and sequence levels.
Scalable Infrastructure
Increase annotation capacity as point-cloud volumes and project requirements grow.
Workflow Integration
Deliver datasets that fit into existing AI, ML, mapping, simulation, and MLOps environments.
SECURITY & DELIVERY
Your 3D Data Stays Controlled From Upload to Delivery
LiDAR and point-cloud datasets can contain sensitive locations, infrastructure, and proprietary information. We keep access controlled and data protected throughout the annotation process.
Encrypted Transfers
Protect LiDAR and point-cloud data during secure upload and download.
Controlled Access
Limit dataset access to authorized people and project teams.
Secure Data Handling
Keep sensitive spatial data within controlled environments throughout the project.
Regulatory Considerations
Support workflows built around applicable privacy, security, and regulatory requirements.
Controlled Delivery
Deliver validated datasets through defined secure channels and required formats.
WHO THIS IS FOR
Built for Teams Working With 3D Data
From autonomous systems and robotics to mapping and industrial applications, we support teams that need reliable 3D data for spatial AI projects.
AI & ML Teams
Build reliable 3D training data for models that need to understand objects, environments, and spatial relationships.
Computer Vision Teams
Create structured 3D datasets for perception, detection, segmentation, recognition, and visual understanding.
Autonomous Systems Teams
Develop annotated spatial data that helps systems understand their surroundings and make sense of complex environments.
Robotics & Industrial AI Teams
Train robots and intelligent machines to recognize objects, spaces, equipment, and physical environments.
Geospatial & Mapping Teams
Turn LiDAR and point-cloud data into structured information for mapping, surveying, navigation, and spatial analysis.
Product & Engineering Teams
Build application-ready datasets that support 3D features, computer vision systems, and spatial intelligence products.
Smart Infrastructure Teams
Create 3D datasets for understanding buildings, infrastructure, facilities, and large physical environments.
Enterprise AI Teams
Scale reliable 3D data operations across AI initiatives, business applications, and production workflows.
FAQ
Questions About LiDAR Annotation?
Find clear answers about LiDAR annotation methods, accuracy, automation, scalability, integration, and how we protect your 3D data.