Link preview
Synthetic-Augmented RGB-D to 3D Object Localization pipeline
This draw io diagram summarizes the perception pipeline I'm building for robotic object localization: - Capture real RGB-D data with an eye-in-hand camera setup. - Bootstrap a small labeled dataset - Fine-tune a YOLO-Seg model - Generate assisted labels for additional real captures - Compose synthetic RGB-D views using masks, depth, camera intrinsics and in-painted backgrounds. - Retrain the segmentation model with the expanded dataset - Input 2D masks, classes and confidences into 3D using depth and camera intrinsics - Extract 3D object localization outputs usable for robotic tasks -- Feedback is welcome! submitted by /u/nettrotten [link] [Kommentare] reddit.com · reddit.com
This draw io diagram summarizes the perception pipeline I'm building for robotic object localization: - Capture real RGB-D data with an eye-in-hand camera setup. - Bootstrap a small labeled dataset - Fine-tune a YOLO-Seg model - Generate assisted labels for additional real captures - Compose synthetic RGB-D views using masks, depth, camera intrinsics and in-painted backgrounds. - Retrain the segmentation model with the expanded dataset - Input 2D masks, classes and confidences into 3D using depth and camera intrinsics - Extract 3D object localization outputs usable for robotic tasks -- Feedback is welcome! submitted by /u/nettrotten [link] [Kommentare]
Comments