Upright adjustment with graph convolutional networks

• URL: http://arxiv.org/abs/2406.00263v1
• Date: Sat, 1 Jun 2024 01:54:57 GMT
• Title: Upright adjustment with graph convolutional networks
• Authors: Raehyuk Jung, Sungmin Cho, Junseok Kwon,
• Abstract summary: We present a novel method for the upright adjustment of 360 images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN)
• Score: 21.103565022124037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel method for the upright adjustment of 360 images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360 images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical representation of the input. We also introduce a novel loss function to address the issue of discrete probability distributions defined on the surface of a sphere. Experimental results demonstrate that our method outperforms fully connected based methods.

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