Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene
  Understanding of Healthcare Facilities
Rohit Mohan José Arce Sassan Mokhtar Daniele Cattaneo Abhinav Valada
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}