3D Point Cloud
Labeling

High-quality data labeling for LiDAR, Radar, or other 3D data. Help your machine learning models see the world in all its complexity.

Talk to an expert

rsip vision

cipia

AMD

shopify

walmart

logitech

Recycleye

viisights

jabra

zkt

1. Assessment

Talk to our Data Solutions Architect and discuss the unique needs of your model.

Get started

2. Custom Annotation
Manual

We develop a comprehensive annotation manual in partnership with you.

Get started

3. Team Training

Our team goes through rigorous training and examination according to the requirements of your project.

Get started

4. Real-Data Iteration

We refine the annotation process through iterative feedback on real data.

Get started

5. Mass Annotation

We increase the scale of the project after achieving the highest possible accuracy in the early stages.

Get started

Point Cloud
Annotation Types

01. Cuboid

Standard for this format, cuboids help tag and track objects in 3D space by specifying their dimensions for AI to interpret height and width.

get in touch

Cuboid Annotation

02. Key Points

Helps add details in a granular manner for tagging even the smallest objects of relevance. This can help AI understand nuance in scenes.

get in touch

Key Points Annotation

03. Polygons

This technique is useful for non-standard shapes, such as production objects, signs, various items on the sidewalk, or anything else that requires moderate precision.

get in touch

04. Polylines

Helps tag roads, pipelines, wires, and other relevant information in your scenes for models to understand infrastructure.

get in touch

Polylines Annotation

05. Segmentation

Annotate everything in a scene, including the background to carefully catalog every little part of the location for your model.

get in touch

Open Your AI’s 3rd Eye with Precise Point Cloud Labeling

Keymakr provides professional data annotation for autonomous vehicles. Our experienced in-house annotation teams will ensure that your machine learning for self-driving car projects go smoothly.

Our proprietary annotation platform features a full suite of annotation techniques that can be adapted for your specific needs. Our annotators are comfortable working with all types, and qualities of data. We can also collect data for you from legal, open-source repositories, or even create bespoke data with our in-house studio.

Get In Touch

What to Expect

EXPERIENCE

Our in-house annotators meticulously label every point, based on a proven management process that’s worked for us for over 9 years.

DOMAIN EXPERTISE

We bring deep experience in various niches, including autonomous vehicles, robotics, construction, and more.

CUTTING-EDGE TECHNOLOGY

We leverage a proprietary annotation platform and industry-leading techniques to streamline the process and ensure efficiency.

DEDICATED SUPPORT

A project manager works in your time zone to streamline communication.

SCALABILITY

We handle projects of any size, from small datasets to massive point cloud collections.

UNMATCHED QA

Our 4-level Quality Assurance process with custom sanity scripting ensures exceptional labeling accuracy.

Teaching AI About Real-World Objects

3D point cloud annotation is the process of labeling and annotating objects for AI projects such as computer vision and machine learning. Point clouds are sets of data points in a 3D coordinate system that represent the surface of an object or scene captured through sensors such as LiDAR or RGB-D cameras.

Annotating 3D point clouds is essential for object recognition, tracking, and scene understanding. The process involves labeling each point with attributes such as object class, size, shape, orientation, and position. This annotation process allows AI models to learn from the data and accurately detect and track objects in real-world scenarios.

Let’s Start

3D Point Cloud Usage

3D Models Creation

3D Models Creation

A point cloud primarily serves as a basis for creating a 3D model. The point cloud can be viewed in 3D as a model, but the point data is often converted to a polygon mesh, since most 3D modeling programs utilize polygons.

Many industries utilize the accurate 3D models formed by point clouds. Architects use 3D models created from laser scans to create as-built drawings and models that capture the building's current condition and layout. For construction and restoration projects, the accuracy of three-dimensional models makes them ideal for measuring distances, areas, and volumes.

For Self-driving Vehicles

3D Point Cloud for Self-driving Vehicles

Increasingly automated driving technologies require vehicles to identify the driving environment, including maps, moving and fixed objects, such as other vehicles, cyclists, pedestrians, traffic lights, and various roads rapidly and accurately.

In order for automatic driving technology to be realized, it is necessary to teach cars how to recognize and interpret this information.

For Smart Cities

3D Point Cloud for Smart Cities

Managing Electric Power Lines. Utilize powerful AI-based classification services of point clouds to ensure safety by detecting vegetation extent, power line conductors, towers, and poles. Using these data, it may be possible to determine where interventions are needed to bring vegetation within safe tolerances by analyzing the distance between them.

Based on lidar or photogrammetry, a power line classification model can classify a point cloud into three key features: power lines, vegetation, and ground. It is this ability which allows the detection of safety-sensitive elements, such as conductors.

🍪 We use third party cookies to personalize content, ads and analyze site traffic. Learn more