Deepen AI - Enterprise
Deepen AI
  • Deepen AI Overview
  • FAQ
  • Saas & On Premise
  • Data Management
    • Data Management Overview
    • Creating/Uploading a dataset
    • Create a dataset profile
    • Auto Task Assignments
    • Task Life Cycle
    • Tasks Assignments
    • Creating a group
    • Adding a user
    • Embed labeled dataset
    • Export
    • Export labels
    • Import Labels
    • Import profile
    • Import profile via JSON
    • Access token for APIs
    • Data Streaming
    • Reports
    • Assessments
  • 2D/3D editors
    • Editor Content
    • AI sense (ML-assisted labeling)
    • Assisted 2d Segmentation
    • Scene Labeling
  • 2D Editor
    • 2D Editor Overview
    • 2D Bounding Boxes
    • 2D Polyline/Line
    • 2D Polygon
      • 2D Semantic/Instance Segmentation
        • 2D Segmentation (foreground/background)
    • 2D Points
    • 2D Semantic Painting
      • Segment Anything
      • Propagate Labels in Semantic Segementation
      • 2D Semantic Painting/Segmentation Output Format
    • 3D Bounding boxes on images
    • 2D ML-powered Visual Object Tracking
    • 2D Shortcut Keys
    • 2D Customer Review
  • 3D Editor
    • 3D Editor Overview
    • 3D Bounding Boxes — Single Frame/Individual Frame
    • 3D Bounding Boxes_Sequence
    • 3D Bounding Boxes Features
      • Label View
      • One-Click Bounding Box
      • Sequence Timeline
      • Show Ground Mesh
      • Secondary Views
      • Camera Views
      • Hide/UnHide Points in 3D Lidar
    • 3D Lines
    • 3D Polygons
    • 3D Semantic Segmentation/Painting
    • 3D Instance Segmentation/Painting
    • Fused Cloud
    • 3D Segmentation (Smart Brush)
    • 3D Segmentation (Polygon)
    • 3D Segmentation (Brush)
    • 3D Segmentation (Ground Polygon)
    • 3D Painting (Foreground/Background)
    • 3D Segmentation(3D Brush/Cube)
    • Label Set
    • 3D Shortcut Keys
    • 3D Customer Review
  • 3D input/output
    • JSON input format for uploading a dataset in a point cloud project.
    • How to convert ROS bag into JSON data for annotation
    • Data Output Format - 3D Semantic Segmentation
    • Data Output Format - 3D Instance Segmentation
  • Quality Assurance
    • Issue Creation
    • Automatic QA
  • Calibration
    • Calibration
    • Charuco Dictionary
    • Calibration FAQ
    • Data Collection for Camera intrinsic Calibration
    • Camera Intrinsic calibration
    • Data Collection for Lidar-Camera Calibration (Single Target)
    • Lidar-Camera Calibration (Single target)
    • Data Collection for Lidar-Camera Calibration (Targetless)
    • Lidar-Camera Calibration (Targetless)
    • Data Collection for Multi Target Lidar-Camera Calibration
    • Multi Target Lidar-Camera Calibration
    • Lidar-Camera Calibration(Old)
    • Vehicle-Camera Calibration
      • Data Collection for Vehicle-Camera Calibration
      • Vehicle Camera Targetless Calibration
      • Data collection for lane based targetless vehicle-camera calibration
      • Lane based Targetless Vehicle Camera Calibration
    • Data Collection for Rough Terrain Vehicle-Camera Calibration
    • Rough Terrain Vehicle-Camera Calibration
    • Calibration Toolbar options
    • Calibration Profile
    • Data Collection for Overlapping-Camera Calibration
    • Overlapping-Camera Calibration
    • Data collection guide for Overlapping Camera Calibration (Multiple-Targets)
    • Overlapping Camera Calibration (Multiple-Targets)
    • Data Collection for Vehicle-3D Lidar calibration
    • Data Collection for Vehicle-2D Lidar calibration
    • Vehicle Lidar (3D and 2D) Calibration
    • Data Collection for Vehicle Lidar Targetless Calibration
    • Data Collection for IMU Lidar Targetless Calibration
    • Vehicle Lidar Targetless Calibration
    • Data Collection for Non Overlapping Camera Calibration
    • Non-Overlapping-Camera Calibration
    • Multi Sensor Visualization
    • Data Collection for LiDAR-LiDAR Calibration
    • LiDAR-LiDAR Calibration
    • Data Collection for IMU Intrinsic calibration
    • IMU Intrinsic Calibration
    • Data Collection for Radar-Camera Calibration
    • Radar-Camera Calibration
    • Data Collection for IMU Vehicle calibration
    • Lidar-IMU Calibration
    • IMU Vehicle Calibration
    • Data Collection for vehicle radar calibration
    • Vehicle radar calibration
    • Calibration Optimiser
    • Calibration list page
    • Data collection for rough terrain vehicle-Lidar calibration
    • Rough terrain vehicle Lidar calibration
    • Surround view camera correction calibration
    • Data Collection for Surround view camera correction calibration
    • Data Collection for Lidar-Radar calibration
    • Lidar Radar Calibration
    • Vehicle Lidar Calibration
    • API Documentation
      • Targetless Overlapping Camera Calibration API
      • Target Overlapping Camera Calibration API
      • Lidar Camera Calibration API
      • LiDAR-LiDAR Calibration API
      • Vehicle Lidar Calibration API
      • Global Optimiser
      • Radar Camera Calibration API
      • Target Camera-Vehicle Calibration API
      • Targetless Camera-Vehicle Calibration API
      • Calibration groups
      • Delete Calibrations
      • Access token for APIs
    • Target Generator
  • API Reference
    • Introduction and Quickstart
    • Datasets
      • Create new dataset
      • Delete dataset
    • Issues
    • Tasks
    • Process uploaded data
    • Import 2D labels for a dataset
    • Import 3D labels for a dataset
    • Download labels
    • Labeling profiles
    • Paint labels
    • User groups
    • User / User Group Scopes
    • Download datasets
    • Label sets
    • Resources
    • 2D box pre-labeling model API
    • 3D box pre-labeling model API
    • Output JSON format
Powered by GitBook
On this page
Export as PDF
  1. 2D Editor
  2. 2D Semantic Painting

Segment Anything

Previous2D Semantic PaintingNextPropagate Labels in Semantic Segementation

Last updated 1 year ago

Segment Anything Model (SAM): A new AI model that can “cut out” any object, in any image, with a single click.

Steps to perform Segment Anything in semantic segmentation:

  1. Click on Label type and select Semantic Painting from the top left menu of the editor.

  2. Click on Paint and select Paint using Segment Anything.

  3. Users can label an object by selecting either Points or Box. The more points that are added, the higher the accuracy of the output. For better accuracy, it is advisable to place more points in the center of the object.

  4. After selecting the point/box, click on the Generate label to preview the results. The user can either accept or reject the label.

  5. Click on Paint and select Save to save all the changes made. It is important to note that for Semantic painting, users must manually label by clicking on Save.

  6. Users can overwrite the painted region by selecting Overwrite painted region.

  7. Users can unselect the Paint using the Segment Anything option and make edits using brush/polygon.