Illuminator: Image-based illumination editing for indoor scene harmonization

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Zhongyun Bao, Gang Fu, Zipei Chen, Chunxia Xiao
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引用次数: 0

Abstract

Illumination harmonization is an important but challenging task that aims to achieve illumination compatibility between the foreground and background under different illumination conditions. Most current studies mainly focus on achieving seamless integration between the appearance (illumination or visual style) of the foreground object itself and the background scene or producing the foreground shadow. They rarely considered global illumination consistency (i.e., the illumination and shadow of the foreground object). In our work, we introduce “Illuminator”, an image-based illumination editing technique. This method aims to achieve more realistic global illumination harmonization, ensuring consistent illumination and plausible shadows in complex indoor environments. The Illuminator contains a shadow residual generation branch and an object illumination transfer branch. The shadow residual generation branch introduces a novel attention-aware graph convolutional mechanism to achieve reasonable foreground shadow generation. The object illumination transfer branch primarily transfers background illumination to the foreground region. In addition, we construct a real-world indoor illumination harmonization dataset called RIH, which consists of various foreground objects and background scenes captured under diverse illumination conditions for training and evaluating our Illuminator. Our comprehensive experiments, conducted on the RIH dataset and a collection of real-world everyday life photos, validate the effectiveness of our method.

Abstract Image

照明器基于图像的照明编辑,实现室内场景协调
光照协调是一项重要但极具挑战性的任务,其目的是在不同光照条件下实现前景和背景之间的光照兼容。目前的大多数研究主要集中在实现前景物体本身的外观(光照或视觉风格)与背景场景的无缝整合,或产生前景阴影。他们很少考虑全局光照一致性(即前景物体的光照和阴影)。在我们的工作中,我们引入了 "Illuminator"--一种基于图像的光照编辑技术。该方法旨在实现更逼真的全局光照协调,确保复杂室内环境中光照一致、阴影可信。照明器包含一个阴影残留生成分支和一个物体照明转移分支。阴影残留生成分支引入了一种新颖的注意力感知图卷积机制,以实现合理的前景阴影生成。物体光照转移分支主要是将背景光照转移到前景区域。此外,我们还构建了一个名为 RIH 的真实世界室内光照协调数据集,其中包括在不同光照条件下捕获的各种前景物体和背景场景,用于训练和评估我们的照明器。我们在 RIH 数据集和一组真实世界的日常生活照片上进行了综合实验,验证了我们方法的有效性。
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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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