Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities
This addresses the data scarcity problem for researchers developing AI systems for healthcare facility scene understanding, though it is incremental as it builds on existing synthetic data methods.
The authors tackled the lack of annotated training data for autonomous healthcare robots by creating Syn-Mediverse, a hyper-realistic multimodal synthetic dataset with over 48,000 images and 1.5 million annotations across five scene understanding tasks, and they evaluated it using state-of-the-art baselines.
Safety and efficiency are paramount in healthcare facilities where the lives of patients are at stake. Despite the adoption of robots to assist medical staff in challenging tasks such as complex surgeries, human expertise is still indispensable. The next generation of autonomous healthcare robots hinges on their capacity to perceive and understand their complex and frenetic environments. While deep learning models are increasingly used for this purpose, they require extensive annotated training data which is impractical to obtain in real-world healthcare settings. To bridge this gap, we present Syn-Mediverse, the first hyper-realistic multimodal synthetic dataset of diverse healthcare facilities. Syn-Mediverse contains over \num{48000} images from a simulated industry-standard optical tracking camera and provides more than 1.5M annotations spanning five different scene understanding tasks including depth estimation, object detection, semantic segmentation, instance segmentation, and panoptic segmentation. We demonstrate the complexity of our dataset by evaluating the performance on a broad range of state-of-the-art baselines for each task. To further advance research on scene understanding of healthcare facilities, along with the public dataset we provide an online evaluation benchmark available at \url{http://syn-mediverse.cs.uni-freiburg.de}