3D Point Cloud
An unprecedented level of precision in visualizing
labeled objects for more accurate detection
and classification for proper dimensions
and label tracking.
FREE TRIAL AVAILABLE
We Meet all 3D Point Annotation Needs
3D point cloud annotation allows you to visualize an object for more detailed detection and classification in order to get the dimension exactly correct.
• Autonomous vehicles - 3D cloud annotation helps autonomous cars perform simultaneous localization and mapping (SLAM) and understand the environment that is around them.
• Agriculture - Use 3D cloud annotation to create an elevation map of the farmland and other maps that describe the terrain. This information will help you understand which areas are producing the most crops.
• Transportation planning - Imagine being able to engineer new roads and highways having all of the knowledge pertaining to width, elevation, way and surface conditions and any other information necessary for construction.
Mindy Support can assist you with all of your 3D point cloud annotation needs by offering services in:
• 3D Box Annotation for Object Detection - We will draw 3D cuboids around the objects you would like the machine to detect.
• 3D Segmentation - This is very useful for capturing the motion of an object in a video.
• Object Classification - Identify and classify all of the objects with the attributes you need for ML algorithms to learn.
• Lane Detection - Distinguish between all of the various lines and road demarcations in an image with polylines.
• LiDAR Data Annotation - We can annotate all of your LiDAR images with 3D cuboids or other techniques of your choice.
We provide you with the full spectrum of segmentation services for the 3D Point Cloud. This includes such features as point size controllers as well as ground and ceiling adjusters.
We can easily match 3D Point Cloud data to corresponding camera images including recognition and labeling of different types of objects.
2D and 3D Annotation
Give your machine learning algorithms the power to learn spatial perception from 3D annotation and relative distance between one object and another with 2D annotation. This is the level of detail that can make or break a project, therefore requiring a high level of precision.