mlsanityData to 14 Oct 2026

VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse

arXiv 2206.11887 ↗First posted 23 Jun 2022cs.CG cs.AIcited by 1

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Abstract (arXiv)

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/

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 11 references that are in the map. First posted on arXiv 23 Jun 2022.

Builds on

8 of 11 references.

References · 8

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,236
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  4. 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 869
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  6. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 380
  7. 2022AKB-48: A Real-World Articulated Object Knowledge Basecited by 82
  8. 2018Using the Jupyter Notebook as a Tool for Open Science: An Empirical Studycited by 9

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Citing papers per year: 2023: 1.

1 paper.