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However, collecting, storing and\u2014in the case of supervised learning\u2014labelling the data is expensive and time-consuming. Additionally, assessing the networks\u2019 generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.<\/jats:p>","DOI":"10.1007\/s10462-024-10722-5","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T08:02:18Z","timestamp":1710835338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Boosting deep neural networks with geometrical prior knowledge: a survey"],"prefix":"10.1007","volume":"57","author":[{"given":"Matthias","family":"Rath","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandru Paul","family":"Condurache","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,19]]},"reference":[{"key":"10722_CR1","doi-asserted-by":"publisher","unstructured":"Agrawal D, Ostrowski J (2022) A classification of g-invariant shallow neural networks. 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