3D Ship Model Generation Algorithm Based on Deep Learning

doi:10.12382/bgxb.2022.B011

Abstract

Abstract: The 3D ship model has a wide range of application scenarios, but its production cost is high. We propose an automatic generation method of 3D ship model based on deep learning: corner points are extracted from a 2D plan with the convolution neural network structure; each corner point is formed into a line segment according to specific rules, and each line segment represents a wall; the line segments are transformed to generate the 3D model; 500 2D ship diagrams are made for model training, and 30 2D ship diagrams for testing. The results show that the new algorithm can accurately detect the corner positions and types of the input image, and generate clear, beautiful and universal 3D models, and the test results demonstrate that the proposed method is effective.

Key words: 3Dshipmodel, deeplearning, shipdiagrams, convolutionneuralnetwork

CLC Number:

U662.9

WANG Xiaoqi, ZHAO Yang, ZHANG Jian, WANG Shuo. 3D Ship Model Generation Algorithm Based on Deep Learning[J]. Acta Armamentarii, 2022, 43(S2): 115-119.

References

［1］张丽.基于虚拟现实技术的船舶三维建模［J］.舰船科学技术,2022,44(9):40-43,56.
ZHANG L.Research on ship 3D modeling design based on virtual reality technology［J］. Ship Science and Technology,2022,44(9): 40-43,56.(in Chinese)
［2］陈波.船舶结构三维建模技术研究与软件开发［D］.大连:大连海事大学,2018.
CHEN B. Research and software development of ship structure 3D modeling technology［D］.Dalian: Dalian Maritime University,2018. (in Chinese)
[3］ RONNEBERGER O, PHILIPP F, THOMAS B.U-net: convolutional networks for biomedical image segmentation[C]∥Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention.Berlin,Germany:Springer, 2015:234-241.
［4］ SUN C,HSIAO C W,SUN M,et al. HorizonNet: learning room layout with 1D representation and panostretch data augmentation[C]∥Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, US:IEEE,2019.
[5］ YANG S T, WANG F E, PENG C H, et al. DuLa-Net: a dual-projection network for estimating room layouts from a single RGB panorama[C]∥Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach, CA, US:IEEE,2019.
[6］ ZOU C, COLBURM A, SHAN Q, et al. Layoutnet: reconstructing the 3d room layout from a single rgb image[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, US:IEEE,2018: 2051-2059.
[7］ ZHANG W D, ZHANG W,ZHANG Y D. GeoLayout: geometry driven room layout estimation based on depth maps of planes[C]∥Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer, 2020.
[8］ FURUKAWA Y, CURLESS B, SEITZ S M, et al. Reconstructing building interiors from images[C]∥Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009: 80-87.
[9］ CABRAL R，FUSUTAKA Y. Piecewise planar and compact floorplan reconstruction from images[C]∥Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, US:IEEE, 2014.
[10］ HUANG J W,DAI A,GUIBAS L J, et al. 3Dlite: towards commodity 3D scanning for content creation[J]. SIGGRAPH Asia, 2017,36(6):1-14.
[11］ ZOLLHFER M, STOTKO P, GRLITZ A, et al. State of the art on 3D reconstruction with RGB-D cameras[J].Computer Graphics Forum,2018, 37(2): 625-652.
[12］ IKEHATA S, YANG H, FURUKAWA Y. Structured indoor modeling[C]∥Proceedings of the IEEE International Conference on Computer Vision.Santiago, Chile:IEEE, 2015: 1323-1331.
[13］ YAN F,NAN L, WONKA P. Block assembly for global registration of building scans[J]. ACM Transactions on Graphics, 2016,35(6):1-11.
[14］ XIAO J, FURUKAWA Y. Reconstructing the world's museums[J]. International Journal of Computer Vision, 2014, 110(3): 243-258.
[15］ VIDANAPATHIRANA M, WU Q R,FURUKAWA Y,et al. Plan2scene: converting floorplans to 3D scenes[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Nashville, TN, US:IEEE, 2021.
[16］ LIU C, WU J J,KOHLI P,et al. Raster-to-vector: revisiting floorplan transformation[C]∥Proceedings of the IEEE International Conference on Computer Vision.Venice, Italy:IEEE,2017.
[17］ RUBINSTEIN R. The cross-entropy method for combinatorial and continuous optimization[J]. Methodology and Computing in Applied Probability, 1999, 1(2): 127-190.

第43卷增刊22022 年11月
兵工学报ACTA ARMAMENTARII
Vol.43Suppl.2Nov. 2022

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