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Acknowledge UnrealCV

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@ -38,5 +38,6 @@ We thank Raffael Theiler and Dario Brescianini for their contributions to ESIM.
This research was supported by by Swiss National Center of Competence Research Robotics (NCCR), Qualcomm (through the Qualcomm Innovation Fellowship Award 2018), the SNSF-ERC Starting Grant and DARPA FLA. This research was supported by by Swiss National Center of Competence Research Robotics (NCCR), Qualcomm (through the Qualcomm Innovation Fellowship Award 2018), the SNSF-ERC Starting Grant and DARPA FLA.
A significant part of ESIM uses components (spline trajectories, inertial measurement unit simulation, various utility functions) from the [ze_oss](https://github.com/zurich-eye/ze_oss) project. A significant part of ESIM uses components (spline trajectories, inertial measurement unit simulation, various utility functions) from the [ze_oss](https://github.com/zurich-eye/ze_oss) project.
ESIM depends on [UnrealCV](https://github.com/unrealcv/unrealcv) for the photorealistic rendering engine.
We also reused some [code samples](https://github.com/JoeyDeVries/LearnOpenGL.git) from the excellent [Lean OpenGL](https://learnopengl.com/) tutorial in our OpenGL rendering engine. We also reused some [code samples](https://github.com/JoeyDeVries/LearnOpenGL.git) from the excellent [Lean OpenGL](https://learnopengl.com/) tutorial in our OpenGL rendering engine.
Finally, ESIM depends on the [Open Asset Import Library (assimp)](https://github.com/assimp/assimp) to load 3D models and Blender scenes within the OpenGL rendering engine. Finally, ESIM depends on the [Open Asset Import Library (assimp)](https://github.com/assimp/assimp) to load 3D models and Blender scenes within the OpenGL rendering engine.