BasicFinder 3D point cloud ADAS Data annotation toolset

  • Support various kinds of high-precision training data services such as 3D point cloud and regional segmentation, with an accuracy rate of 99.7%
  • The professional data factory with more than 2000 employees shall mark, and the manual operation shall ensure the unified standard and accurate mark
  • Hundreds of thousands of people crowdsourced to cover more than 120 cities, quickly responding to various data needs
  • Powerful and easy to use template editor and iframe custom features to help you easily publish projects
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Fully meet the annotation requirements

BasicFinder ADAS includes seven major categories of commonly used self-driving tagging services and supports customization
3D point cloud annotation
3D point cloud annotation
3D object annotation
3D object annotation
Object tracking
Object tracking
Polygon annotation
Polygon annotation
Semantic segmentation
Semantic segmentation
Rectangular annotation
Rectangular annotation
Traffic Lane annotation
Traffic Lane annotation

In the 3D point cloud images collected by lidar, 3D box is used to mark target, such as vehicles, pedestrians and roadblocks

3D point cloud annotation

Use 3D box to mark the target object in 2D picture, such as car, bus, truck, pedestrian etc.

3D object annotation

In video, a rectangular box is used to mark the target ojbect and track the position.

Object tracking

Polygons are used in images to mark the target object with irregular shape, such as vehicle contour, identification plate, driving area, sky, etc

Polygon annotation

Image is classified according to its content, and different object regions are labeled by pixel-level segmentation

Semantic segmentation

Rectangular box is used to mark out the target, such as pedestrians and vehicles in image.

Rectangular annotation

Use straight lines or curves to mark the lane lines in the picture

Traffic Lane annotation

The four advantages

  • Factory Annotation - crowdsourcing collection

    More than 2,000 experienced full-time annotators and Hundreds of thousands of crowdsourcing freelancers covering more than 120 cities provide data services

  • 99.7% percent accuracy

    The BasicGuard strictly controls the quality and ensures the 99.7% accuracy and the high quality of data

  • Safty and convenient

    Drag-and-drop template configuration tools, and support 3rd party annotation tool API access, strong protection of data security

  • Independent management

    Self helped task publish, result download and monitor the full annotation procedure

How do we help you

obtain project requirements
BasicFinder will provide one-to-one project managers for every project. With professional communication procedure, accurate positioning of project requirements, efficient matching of optimal execution plans, full-process follow-up of project progress, and real-time control of project quality, BasicFinder will set up a team of project managers
Quickly configure production projects
The powerful BasicFinder ADAS suite fully covers the tagging needs of autonomous driving enterprises, quickly configuring projects and distributing them to factory annotators or crowdsourced users
Label/collect data
The BasicFinder data factory has more than 2,000 experienced professional annotators and crowdsourced users in more than 120 cities who can quickly respond to data services
BasicGuard programmatic quality control engine
Centralized management and unified acceptance standards of the data factory are adopted, and the quality of data annotation and collection is strictly controlled by the BasicGuard programmatic quality control engine
Rich data delivery
The final qualified data will be prepared in strict accordance with the requirements, supporting multiple formats such as. Json.jpg.png.xml to submit data

The choice of well-known autonomous driving research institutions

BasicFinder ADAS annotation suite It has served hundreds of autonomous driving research institutions