Visual inspection of complex mechanical assemblies based on Siamese networks for 3D point clouds

This paper proposes a solution for the problem of visual mechanical assembly inspection by processing point cloud data acquired via a 3D scanner. The approach is based on deep Siamese neural networks for 3D point clouds. To overcome the requirement for a large amount of labeled training data, only synthetically generated data is used for training and validation. Real-acquired point clouds are used only in testing phase.