iMerit's 3D Point Cloud Solution

Discover how iMerit’s cutting-edge 3D point cloud technology automates annotation workflows by integrating multi-sensor data from LiDAR, Radar, and cameras. This video showcases innovative automation features designed to improve annotation accuracy, streamline validation, and enhance object detection. Key Features Covered in the Demo: Multi-Sensor Data Fusion: - Utilizes LiDAR, Radar, and camera data for a comprehensive perception system. - Enhances object detection and annotation accuracy with multi-source data integration. Automated Point Cloud Merging: - Merges multi-frame point cloud data into a single, unified coordinate system. - Eliminates manual frame traversal, offering a holistic view of object sequences. - Improves efficiency by allowing users to visualize aggregated data over time. Validation Automation: - Generates automatic validation reports summarizing annotation quality. - Identifies errors in high-quality annotation to ensure precision and consistency. 3D-to-2D Projection Automation: - Projects 3D cuboids onto 2D images, aligning LiDAR and camera data. - Simplifies object detection by providing clear visual references. - Automates bounding box creation, eliminating manual annotation steps. Intensity Feature: - Intensity feature to bring forward objects on the scene with reflective surfaces. Helps in use cases such as "Lane Detection, Traffic Signs, Traffic Cones, Traffic Signals, VRU wearing reflective gear..." - Enhances object visibility by adjusting contrast settings. Machine Learning-Powered Point Cloud Segmentation: - Automatically differentiates point cloud data into ground and non-ground points. - Reduces manual annotation errors and refines labeling accuracy. Comprehensive Drawable Tools for 3D Annotation: - Supports 2D polygons, 2D bounding boxes, 2D cuboids, 3D polygons, 3D bounding boxes, and 3D cuboids and paintbrush - Linearly interpolates cuboid generation across frames for seamless tracking. - Provides attribute propagation for efficient scene labeling.