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SMA-Net: Deep learning-based identification and fitting of CAD models from point clouds

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Abstract

Identification and fitting is an important task in reverse engineering and virtual/augmented reality. Compared to the traditional approaches, carrying out such tasks with a deep learning-based method have much room to exploit. This paper presents SMA-Net (Spatial Merge Attention Network), a novel deep learning-based end-to-end bottom-up architecture, specifically focused on fast identification and fitting of CAD models from point clouds. The network is composed of three parts whose strengths are clearly highlighted: voxel-based multi-resolution feature extractor, spatial merge attention mechanism and multi-task head. It is trained with both virtually-generated point clouds and as-scanned ones created from multiple instances of CAD models, themselves obtained with randomly generated parameter values. Using this data generation pipeline, the proposed approach is validated on two different data sets that have been made publicly available: robot data set for Industry 4.0 applications, and furniture data set for virtual/augmented reality. Experiments show that this reconstruction strategy achieves compelling and accurate results in a very high speed, and that it is very robust on real data obtained for instance by laser scanner and Kinect.

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Authors and Affiliations

  1. LISPEN, Arts et Métiers Institute of Technology, 13617, Aix-en-Provence, France

    Sijie Hu, Arnaud Polette & Jean-Philippe Pernot

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  1. Sijie Hu
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  2. Arnaud Polette
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Appendix 1

Appendix 1

See Tables 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and 16.

Table 5 Parameter name and index relationship of the chairs in the furniture dataset
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Table 6 Parameter name and index relationship of the tables in the furniture dataset
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Table 7 Result of Chair_1 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 5
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Table 8 Result of Chair_2 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 5
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Table 9 Result of Chair_3 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 5
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Table 10 Result of Chair_4 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 5
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Table 11 Result of Table_1 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 6
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Table 12 Result of Table_2 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 6
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Table 13 Result of Table_3 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 6
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Table 14 Result of Table_4 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 6
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Table 15 Result of Table_5 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 6
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Table 16 Result of Table_6 in the furniture test dataset (\(N_\mathrm{test}^\mathrm{Furniture} = 3000\)), with the name of each parameter \(p_i\) provided in Table 6
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Hu, S., Polette, A. & Pernot, JP. SMA-Net: Deep learning-based identification and fitting of CAD models from point clouds. Engineering with Computers 38, 5467–5488 (2022). https://doi.org/10.1007/s00366-022-01648-z

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