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With the advance of modern technology and computer power, processing and analysis of 3D shapes becomes a ubiquitous task due to fast acquisition and frequent use of 3D data. Recent developments of machine learning bring many state-of-the-art results in image processing. However, extension of deep learning methods in 3D shape analysis is still in an early state due to the challenge of non-Euclidean structures of 3D shapes. The mini symposium aims at enhancing interaction of scholars working on learning methods for 3D shape analysis.