Abstract
We propose a novel lightweight deep learning-based method that efficiently recognizes feature points with significantly shorter preprocessing time. Our method encodes CL points as matrices and stores them as text files. We have developed a neural network with an Encoder-Decoder architecture, named EDFP-Net, which takes the encoding matrices as input, extracts deeper features using the Encoder, and recognizes feature points using the Decoder. Our experiments on industrial parts demonstrate the superior efficiency of our method.
We thank Dr. Pengcheng Hu for his helpful discussion and the dataset. This work is partially supported by National Key Research and Development Program of China under Grant 2020YFA0713703, NSFC (Nos. 11688101, 61872332) and Fundamental Research Funds for the Central Universities.
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Chen, SP., Ma, HY., Shen, LY., Yuan, CM. (2024). A Lightweight Model for Feature Points Recognition of Tool Path Based on Deep Learning. In: Hu, SM., Cai, Y., Rosin, P. (eds) Computer-Aided Design and Computer Graphics. CADGraphics 2023. Lecture Notes in Computer Science, vol 14250. Springer, Singapore. https://doi.org/10.1007/978-981-99-9666-7_4
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