Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation
This work addresses data acquisition challenges for deep learning in industrial robotics, though it appears incremental by building on existing sim2real methods.
The paper tackles the sim2real gap in industrial robotics by introducing a physically-based structured light simulation system that generates realistic RGB and depth images, creating a dataset for robotic grasping tasks and showing improved performance in object detection and instance segmentation.
Despite the substantial progress in deep learning, its adoption in industrial robotics projects remains limited, primarily due to challenges in data acquisition and labeling. Previous sim2real approaches using domain randomization require extensive scene and model optimization. To address these issues, we introduce an innovative physically-based structured light simulation system, generating both RGB and physically realistic depth images, surpassing previous dataset generation tools. We create an RGBD dataset tailored for robotic industrial grasping scenarios and evaluate it across various tasks, including object detection, instance segmentation, and embedding sim2real visual perception in industrial robotic grasping. By reducing the sim2real gap and enhancing deep learning training, we facilitate the application of deep learning models in industrial settings. Project details are available at https://baikaixinpublic.github.io/structured light 3D synthesizer/.