Precision in three dimensions drives safer autonomy. Learning Spiral offers expert 3D point cloud annotation for LiDAR-generated data, including cuboid, semantic segmentation, and polyline labeling. Our annotators accurately capture object shape, distance, and spatial structure, enabling robust training data for autonomous vehicles, robotics, and mapping applications that demand exceptional depth and geometric accuracy.
3D Point Cloud Annotation
Object Detection
Reliable object detection begins with reliable annotation. Learning Spiral provides high-accuracy labeling for vehicles, pedestrians, cyclists, and roadside objects across images, video, and sensor data. Using bounding boxes, polygons, and cuboids, our teams deliver clean, consistent datasets that help AI models identify and classify objects with speed and precision in real-world environments.
Tracking
Understanding motion is key to autonomous intelligence. Learning Spiral delivers precise object tracking annotation across video frames and multi-sensor sequences, maintaining consistent IDs for vehicles, pedestrians, and obstacles. Our tracking datasets support behavior prediction, trajectory analysis, and temporal fusion, helping AI systems anticipate movement and make safer, smarter real-time driving decisions.
Segmentation
Every pixel matters for accurate perception. Learning Spiral offers detailed semantic and instance segmentation services, labeling roads, lanes, obstacles, and scene elements at the pixel level. Our segmentation datasets provide fine-grained scene understanding, empowering computer vision models used in autonomous vehicles, robotics, and advanced driver-assistance systems to interpret complex environments accurately.