Slicing point cloud incrementally for Additive Manufacturing via online learning

Abstract

This paper reports an algorithm to chop point cloud into layer-wise slices for additive manufacturing. It starts with intersecting slicing plane with the 3D input points, generating planar samples. Then, an online learning model, known as competitive segments representation (CSR), extracts their implicit topology and distribution. CSR structure is a restricted graph that equals to multiple polylines, which are meanwhile piecewise linear approximation to the principal curves of samples. Edge segments of CSR compete with each other for representing consecutively given samples. They dynamically move, grow, shrink or rewire subject to several heuristic rules. Those rules are designed to depress abnormal data, enable lifelong learning, recover salient feature and ensure correct topology. Assembling them together allows online tracking of changing curves. Once CSR converges on one slice, learnt curves are reused as initial estimation for the next. By this practice, shape coherence of successive slices is efficiently utilized, and the ongoing learning output all subsequent slices incrementally. We have verified the feasibility of proposed algorithm both on synthesized data and scanned points.

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Acknowledgements

This work was supported by the National High Technology Research and Development Program of China (Grant No. 2015AA042502). We are grateful to Xu Jinting for providing the PLSP implementation.

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Correspondence to Kaihua Xue.

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Yang, T., Yao, S. & Xue, K. Slicing point cloud incrementally for Additive Manufacturing via online learning. Neural Comput & Applic 32, 11521–11541 (2020). https://doi.org/10.1007/s00521-019-04640-9

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Keywords

  • Additive Manufacturing
  • Curve reconstruction
  • Topology learning
  • Principal curve