论文标题
vrkitchen2.0-Indoorkit：Omniverse增强室内场景的教程
VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse
论文作者
论文摘要
随着3D建模软件和游戏引擎的最新模拟进展，许多研究人员专注于在虚拟环境中体现AI任务。但是，研究社区缺乏一个可以轻松地为室内场景合成和使用各种算法进行基准测试的平台。同时，与计算机图形相关的任务需要一个工具包来实现高级合成技术。为了促进室内场景构建方法及其潜在的机器人应用的研究，我们介绍了Inoorkit：NVIDIA Omniverse的内置工具包，为室内场景构建，场景随机化和动画控制提供了灵活的管道。此外，在动画软件Inoorkit中将Python编码相结合，可帮助研究人员创建实时培训，控制化身和机器人技术。该工具包的源代码可从https://github.com/realvcla/vrkitchen2.0-tutorial获得，并且该教程以及工具包可在https://vrkitchen20-tutorial.readthedocs.io/en/上找到。
With the recent progress of simulations by 3D modeling software and game engines, many researchers have focused on Embodied AI tasks in the virtual environment. However, the research community lacks a platform that can easily serve both indoor scene synthesis and model benchmarking with various algorithms. Meanwhile, computer graphics-related tasks need a toolkit for implementing advanced synthesizing techniques. To facilitate the study of indoor scene building methods and their potential robotics applications, we introduce INDOORKIT: a built-in toolkit for NVIDIA OMNIVERSE that provides flexible pipelines for indoor scene building, scene randomizing, and animation controls. Besides, combining Python coding in the animation software INDOORKIT assists researchers in creating real-time training and controlling avatars and robotics. The source code for this toolkit is available at https://github.com/realvcla/VRKitchen2.0-Tutorial, and the tutorial along with the toolkit is available at https://vrkitchen20-tutorial.readthedocs.io/en/
