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The ability to apply deep learning techniques to non\u2010Euclidean domains including graphs, manifolds, and point clouds is made possible by non\u2010Euclidean deep learning. The use of non\u2010Euclidean deep learning is rapidly expanding to study real\u2010world datasets that are intrinsically non\u2010Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non\u2010Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field\u2019s history. Modern state\u2010of\u2010the\u2010art methods have been described briefly and categorized by application fields. It also highlights the model\u2019s shortcomings in tables and graphs and shows different real\u2010world applicability. 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