Outdoor illumination estimation via all convolutional neural networks. (March 2021)

• Record Type: Journal Article
• Authors: Zhang, Kejun; Li, Xinxin; Jin, Xin; Liu, Biao; Li, Xiaodong; Sun, Hongbo
• Is Part Of: Computers & electrical engineering. Volume 90(2021)
• Publication Date: 2021-03
• Subjects: Sun orientation estimation -- Luminance channel -- All convolutional neural network -- Network compression; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.106987
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: When inserting a virtual object into an outdoor image, the recovery of sun orientation has a critical effect on the fusion of virtual objects and real scenes. For the problem of sun orientation estimation from a single outdoor image, a new end-to-end learning method is proposed in this paper. A luminance channel of sky region is introduced into the input to enhance the extracted image features. A network only consists of convolutional layers is designed to improve the ability of extracting image features. Pruning and quantization are used to compress the network, resulting in a large reduction in the number of network parameters and the storage space, only with a slight loss of precision. Graphical abstract: Highlights: An all convolutional neural network for sun orientation estimation from an image. Using the relationship between the brightness of the sky and the sun orientation. We simplify the network structure and improves the network performance.