Upright adjustment with graph convolutional networks
<|reference_start|>Upright adjustment with graph convolutional networks: 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.<|reference_end|>
@article{jung2024upright,
    title={Upright adjustment with graph convolutional networks},
    author={Raehyuk Jung, Sungmin Cho, Junseok Kwon},
    journal={arXiv preprint arXiv:2406.00263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00263},
    primaryClass={cs.CV}
}