Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation

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://baikaixin-public.github.io/structured_light_3D_synthesizer/

Authors

• Kaixin Bai
• Lei Zhang 0198
• Zhaopeng Chen
• Fang Wan
• Jianwei Zhang 0001

Keywords

• Deep learning
• Training
• Adaptation models
• Three-dimensional displays
• Service robots
• Annotations
• Transfer learning
• Structured Illumination
• Synthetic Simulation
• Synthetic Data Simulation
• Sim2real Gap
• Object Detection
• Visual Perception
• Depth Images
• Domain Adaptation
• Industrial Settings
• Realistic Images
• Instance Segmentation
• Real Depth
• 3D Reconstruction
• Real-world Data
• Point Cloud
• Projector
• RGB Images
• Real-world Datasets
• Depth Map
• Robotic Tasks
• Gray Code
• Vision Tasks
• Stereo Camera
• Object Database
• Perceptual Task
• Pose Estimation
• Time-of-flight Sensors
• Gray Images
• Visual Perception Tasks