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Hybrid Rendering Architecture for Realtime and Photorealistic Simulation of Robot-Assisted Surgery.

Sebastijan Müller¹, Andreas Bihlmaier¹, Stephan Irgenfried¹

¹Institute for Anthropomatics and Robotics (IAR) - Intelligent Process Control and Robotics Lab (IPR), Karlsruhe Institute of Technology (KIT), Germany.

Studies in Health Technology and Informatics

|April 6, 2016

Summary

This study introduces a method for unified 3D model creation for both real-time and photorealistic rendering in surgical robotics simulations. This approach enhances testing by enabling high-fidelity image generation for computer vision algorithms.

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Area of Science:

Computer Graphics
Robotics
Medical Simulation

Background:

Real-time rendering is adequate for robot-assisted surgery simulation and testing.
High-fidelity images are crucial for end-to-end system testing, particularly for computer vision algorithms.
Integrating real-time and photorealistic rendering presents challenges in unified model creation.

Purpose of the Study:

To present a method for combining real-time and non-real-time (photorealistic) rendering using open-source software.
To address the obstacle of creating unified models suitable for both rendering paradigms.
To demonstrate a practical application of this method using a medical phantom model.

Main Methods:

Developed a modeling pipeline utilizing open-source tools and established open standards for data exchange.
Focused on creating models compatible with both real-time rendering engines (e.g., Gazebo) and raytracers (e.g., Cycles).
Employed a unified model approach for the OpenHELP medical phantom.

Main Results:

Successfully demonstrated a method to combine real-time and photorealistic rendering.
Created a unified model of the OpenHELP phantom usable in the Gazebo robotics simulator.
Showcased the capability to render the unified model with enhanced visual fidelity in the Cycles raytracer.

Conclusions:

The developed open-source pipeline enables seamless integration of real-time and photorealistic rendering.
This approach facilitates more comprehensive testing of robot-assisted surgery systems, especially for computer vision components.
Unified modeling is key to bridging the gap between simulation needs and high-fidelity visualization.