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Point Cloud Annotation Service

3D Sensor Fusion &
Point Cloud
Data Labeling Service

Expert 3D sensor fusion data annotation services that enable your computer vision systems to accurately perceive and interact with the world.

3D Sensor Fusion Annotation Service

3D Sensor Fusion Annotation

Our experienced teams provide precise and reliable 3D sensor fusion and point cloud data annotation services, covering voxel-perfect 3D cuboid, semantic segmentation, sensor fusion annotation, and other custom labels to meet your precise data labeling needs.

3D Bounding Box Annotation Service
Cuboid Annotation Service

3D Bounding Box  / Cuboid Annotation

We deliver high-quality 3D cuboids in point cloud data, with accurate shape, size and location information in 3D space.

2D & 3D Linking
2D & 3D Linking

2D & 3D Linking

We provide enriched, multi-sensor datasets with linked 2D bounding boxes and 3D cuboids for robust perception algorithms.

3D Semantic Segmentation Service
3D Instance Segmentation Service

3D Semantic Segmentation

We create per-point labels that enable your ML / CV models to discern objects and environmental features with high granularity.

3D Object Tracking Annotation Service
3D Object Detection Annotation

3D Object Tracking Annotation

We deliver object detection and tracking annotations in a series of sensor fusion frames that train your model to see and track objects across time and space. 

Lane & Drivable Area Annotation Service
Lane & Drivable Area Annotation Service

Lane & Drivable Area Annotation

We map out the drivable surface and lanes in 3D sensor fusion data, providing key navigation information for autonomous systems to understand complex traffic scenarios.

Our Expertise in Point Cloud & 3D Sensor Fusion Annotation Services

Our Expertise


Labeled datasets of all types


Selected global annotation teams


Data quality review


Years of training data experience

Proprietary Data Annotation Platform

BasicAI Cloud, an AI-powered data annotation platform, provides advanced tools specialized for LiDAR 3D point cloud and multi-sensor data labeling. It enables our data annotators to efficiently collaborate on large-scale 3D  sensor fusion annotation workflows while maintaining label quality and consistency.

AI-Powered Annotation Toolset

Auto Annotation, Segmentation and Object Tracking

Auto Ground Detection

AI-assisted Annotation

Online Calibration

Quality Assurance

Configurable QA Rules

Real-time Quality Check

Batch Quality Check

Consecutive Frames Review

Quality Assurance
BasicAI Cloud - Sensor Fusion Annotation


Large Project Support

Cloud Storage and Online Import / Export

Workflow & Performance Management

Roles & Priviledges Management


Use Case

The Essential for Your CV Model

3D Sensor Fusion data annotation helps ML / CV model training to interpret the data captured by cameras and 3D sensors. It involves labeling raw sensor data to provide context and meaning, which allows AI models to learn how to recognize and interpret similar data in the future.

Autonomous Driving & ADAS

Autonomous vehicles use cameras and LiDAR sensors to identify and classify objects, understand the speed and direction of other objects. This data, when combined with AI models, helps the vehicle make informed decisions about how to navigate the environment safely and efficiently. By learning from annotated data, the AI model can create a detailed 3D representation of the environment and make informed decisions about how to navigate.  Learn More

Autonomous Driving & ADAS
Autonomous Driving & ADAS

Semantic segmentation of cars, bikes, pedestrians, roads, curbs, buildings, vegetation, etc., for object detection and scene segmentation.

3D bounding boxes around vehicles, people, traffic signs, etc., for object localization and tracking.

Polygons and poly-lines of drivable surfaces and lanes, for path planning and navigation.

Annotated consecutive frames showing vehicle movement over time, for motion forecasting models.

Classification of weather conditions like rain, fog, snow to improve perception in adverse environments.

Smart Cities
Smart Cities

Smart Cities

Smart Cities use 3D sensor fusion and LiDAR AI models to monitor and manage urban infrastructure. This can include traffic management, where the technology is used to monitor traffic flow and optimize traffic signals, or infrastructure inspection, where it's used to identify potential issues with buildings, bridges, or roads. Annotated datasets can help AI models identify specific elements of the urban landscape, such as vehicles, pedestrians, buildings, or traffic signals. This information can be used for various purposes, like traffic management or infrastructure maintenance.   Learn More

Bounding boxes for pedestrians, cyclists, scooters to analyze mobility patterns.

Instance segmentation of parking spots, traffic lights, road signs, bus stops, for asset management.

Terrain labeling for accessibility features - curb ramps, crosswalks, sidewalk width.

Attribute tags like pavement type, lighting conditions, tree species for surveys.

Time-based labels to identify congestion, incidents, construction events.


Robots use multiple sensors to navigate their surroundings, identify objects, and perform tasks. This is especially useful in environments that are hazardous or difficult for humans to operate in, such as factories, power plants, space exploration, or underwater exploration. Trained CV models can be used to help robots understand their environment and interact with it effectively. Annotated data can help train the robot's AI model to recognize these specific parts, tools, or obstacles, and determine appropriate actions.

Smart Retail
Smart Retail

Point labels of objects the robot must interact with - mugs, keyboards, doors, for grasp planning and manipulation.

Annotated human poses over time to train motion prediction for safe planning.

Normal vectors on table surfaces to align objects and constrain movement.

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Get the Essential
Training Data for Your CV Model

Compliances and AI Partners

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