VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse
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/
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingIndoor Scene SynthesisSimilar Papers 제목 키워드 기반
VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents. Previously…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Task PlanningInverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image
We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we create a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatiall…
GPUInverse RenderingMesh Based Semantic Modelling for Indoor and Outdoor Scenes
Semantic reconstruction of a scene is important for a variety of applications such as 3D modelling, object recognition and autonomous robotic navigation. However, most object labelling methods work in the image domain an…
ObjectObject RecognitionFunction2Scene: 3D Indoor Scene Layout from Functional Specifications
Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how the space is used. Yet in real interior design, a layout is judged by …
Indoor Scene SynthesisIndoorR2X: Indoor Robot-to-Everything Coordination with LLM-Driven Planning
Although robot-to-robot (R2R) communication improves indoor scene understanding beyond what a single robot can achieve, R2R alone cannot overcome partial observability without substantial exploration overhead or scaling …
Robot Task PlanningScene Understanding