CHS: Small: Learning to Automatically Design Interior Spaces

CHS：小：学习自动设计室内空间

• 批准号: 1907547
• 负责人: Daniel Ritchie
• 金额: $ 49.83万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907547&HistoricalAwards=false
• 关键词: CHS; Small; Learning; Automatically; Design

People spend a large part of their lives indoors, in bedrooms, living rooms, offices, kitchens, etc. The demand for virtual versions of these spaces has never been higher; robotics, computer vision, architecture, interior design, virtual and augmented reality -- all of these fields need to create high-fidelity digital instances of real-world indoor scenes. To meet this need, this project will develop new generative models of indoor scenes that can rapidly synthesize novel environments. To achieve this goal, a scene synthesis system should be data driven, be able to quickly generate a variety of plausible and visually appealing results, and be user-controllable. While prior work has addressed indoor scene synthesis, no existing approach satisfies all of these requirements. Not only will this project achieve that goal, it also includes efforts to use the new software system for training robots to navigate. Broader impact of project outcomes will be enhanced through industrial technology transfer in collaboration with two furniture and interior design companies. The research will create freely available online demos, and will engage and mentor female students as research assistants.The first component of the envisaged system will be a new scene generative model based on deep convolutional neural networks that unifies a detailed, image-based representation of scenes based on floor plans with a discrete, symbolic representation of scenes based on object relationship graphs, thereby gaining the benefits of both to generate a variety of plausible scenes. Convolutions on both graphs and images will be employed to make synthesis decisions based on the relevant spatial context in the scene; the resulting model will be fast, controllable, and fully data driven. The system's second component will be a model of the visual compatibility of scene objects, which is necessary for generating visually appealing scenes. This model will exploit a convolutional network to analyze rendered views of the scene, capturing the visual appearance of the scene and objects in it; the network will be trained on a new dataset of professionally designed interior scenes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们在室内，卧室，客厅，办公室，厨房等中度过了很大一部分生活。这些空间的虚拟版本的需求从未如此高。机器人技术，计算机视觉，体系结构，室内设计，虚拟和增强现实 - 所有这些领域都需要创建现实世界中室内场景的高保真数字实例。为了满足这一需求，该项目将开发新的室内场景模型，这些模型可以快速综合新的环境。为了实现这一目标，场景合成系统应该是数据驱动的，能够快速生成各种合理且视觉上吸引人的结果，并可以控制用户。尽管先前的工作已经解决了室内场景的综合，但现有的方法不满足所有这些要求。该项目不仅可以实现该目标，还包括使用新软件系统进行培训机器人进行导航的努力。 通过与两家家具和室内设计公司合作，将通过工业技术转移来增强项目成果的更广泛影响。 这项研究将创建免费的在线演示，并将吸引并指导女学生作为研究助理。设想的系统的第一个组成部分将是一种基于深度卷积神经网络的新场景生成模型，该模型将基于离散的，基于对象关系图的象征性的场景，基于平面图的场景统一详细的，基于图像的场景表示，从而使场景均产生各种范围，从而获得了综合的范围。图形和图像上的卷积都将用于基于场景中相关的空间上下文做出综合决策。最终的模型将是快速，可控且完全数据驱动的。系统的第二个组件将是场景对象的可视兼容性的模型，这对于生成视觉吸引力的场景是必不可少的。该模型将利用一个卷积网络来分析场景的渲染视图，捕获场景和对象的视觉外观；该网络将在新的专业设计的室内场景的新数据集中进行培训。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。

项目成果

Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms

• DOI: 10.1111/cgf.14357
• 发表时间: 2021-08
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Kai Wang;Xianghao Xu-;Leon Lei;Selena Ling;Natalie Lindsay;Angel X. Chang;M. Savva;Daniel Ritchie

Learning Generative Models of 3D Structures

• DOI: 10.1111/cgf.14020
• 发表时间: 2020-05-01
• 期刊: COMPUTER GRAPHICS FORUM
• 影响因子: 2.5
• 作者: Chaudhuri, Siddhartha;Ritchie, Daniel;Zhang, Hao
• 通讯作者: Zhang, Hao