Virtual home staging and relighting from a single panorama under natural illumination

IF 2.4 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guanzhou Ji, Azadeh O. Sawyer, Srinivasa G. Narasimhan
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Abstract

Virtual staging technique can digitally showcase a variety of real-world scenes. However, relighting indoor scenes from a single image is challenging due to unknown scene geometry, material properties, and outdoor spatially-varying lighting. In this study, we use the High Dynamic Range (HDR) technique to capture an indoor panorama and its paired outdoor hemispherical photograph, and we develop a novel inverse rendering approach for scene relighting and editing. Our method consists of four key components: (1) panoramic furniture detection and removal, (2) automatic floor layout design, (3) global rendering with scene geometry, new furniture objects, and the real-time outdoor photograph, and (4) virtual staging with new camera position, outdoor illumination, scene texture, and electrical light. The results demonstrate that a single indoor panorama can be used to generate high-quality virtual scenes under new environmental conditions. Additionally, we contribute a new calibrated HDR (Cali-HDR) dataset that consists of 137 paired indoor and outdoor photographs. The animation for virtual rendered scenes is available here.

Abstract Image

在自然光下,通过单个全景图进行虚拟家居分期和重新照明
虚拟舞台技术可以通过数字技术展示现实世界中的各种场景。然而,由于未知的场景几何形状、材料属性和室外空间变化的照明,从单一图像重新照明室内场景具有挑战性。在本研究中,我们使用高动态范围(HDR)技术来捕捉室内全景及其配对的室外半球照片,并开发了一种用于场景重新照明和编辑的新型反渲染方法。我们的方法由四个关键部分组成:(1) 全景家具检测和移除;(2) 自动地板布局设计;(3) 使用场景几何图形、新家具对象和实时室外照片进行全局渲染;(4) 使用新相机位置、室外照明、场景纹理和电光进行虚拟分期。结果表明,在新的环境条件下,可以使用一张室内全景图生成高质量的虚拟场景。此外,我们还提供了一个新的校准 HDR(Cali-HDR)数据集,该数据集由 137 张成对的室内和室外照片组成。虚拟渲染场景的动画在此提供。
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来源期刊
Machine Vision and Applications
Machine Vision and Applications 工程技术-工程:电子与电气
CiteScore
6.30
自引率
3.00%
发文量
84
审稿时长
8.7 months
期刊介绍: Machine Vision and Applications publishes high-quality technical contributions in machine vision research and development. Specifically, the editors encourage submittals in all applications and engineering aspects of image-related computing. In particular, original contributions dealing with scientific, commercial, industrial, military, and biomedical applications of machine vision, are all within the scope of the journal. Particular emphasis is placed on engineering and technology aspects of image processing and computer vision. The following aspects of machine vision applications are of interest: algorithms, architectures, VLSI implementations, AI techniques and expert systems for machine vision, front-end sensing, multidimensional and multisensor machine vision, real-time techniques, image databases, virtual reality and visualization. Papers must include a significant experimental validation component.
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