A lightweight framework for one-stop hull reverse modeling from large-amount point cloud data

doi:10.11996/JG.j.2095-302X.2025061200

Abstract

Abstract:

There is a practical need for reverse modeling technology of ship hulls based on large-scale point clouds in applications such as digital twins and hydrodynamic performance analysis. Currently, few specialized methods addressed large-scale point cloud data for ship hull modeling, and conventional approaches usually relied on neural network models with high training costs or foreign commercial software. In this case, designing a lightweight, one-stop method for hull reverse modeling is valuable: initially, the ship hull was meshed to compress the point cloud data, reducing the data volume by over 90% while preserving the hull’s macro geometric features, thereby significantly decreasing the computational cost for the following algorithms; secondly, contour line detection and fitting algorithms were designed based on the hull’s geometric features, utilizing the least squares method for contour correction to ensure its completeness and smoothness; finally, a smoothing algorithm based on slope anomaly detection was adopted, which efficiently performed automatic smoothing of the hull’s longitudinal and vertical form lines through three steps: slope anomaly detection, preliminary correction, and deep smoothing. The method was validated on two ship models. Compared to the reference models established using commercial CAD software, the principal dimension errors (+0.24% to +2.68%) and displacement volume deviations (-1.05% to -0.88%) were only minor and all within an acceptable range. The entire process was completed within 5 minutes on a conventional computer. The method demonstrated potential for application in related fields such as hull reverse modeling, ship digitalization, and hull form smoothing for 3D generative large models.

Key words: 3D point cloud data, hull-form fairing, reverse modeling, mesh down-sampling, hull form

CLC Number:

U663.2
TP391

HUANG Yongyu, DU Lin, QIANG Yiming, DING Jun. A lightweight framework for one-stop hull reverse modeling from large-amount point cloud data[J]. Journal of Graphics, 2025, 46(6): 1200-1208.

Figures/Tables 17

Fig. 1 Comparison of regular methods and conducted research

Fig. 2 This article designs a brief flowchart of the algorithm

Fig. 3 Testing ship models ((a) Ship model A; (b) Ship model B)

Table 1 Specification of the testing ship models

模型	L_WL/m	B_WL/m	T/m	V/m³	C_B
A	61.96	10.82	4.12	1964.39	0.7112
B	154.79	23.63	6.00	13457.36	0.6132

Table 2 Characteristics of artificial noise on testing models

模型	点云数量	坐标轴	μ	σ
A	115 737	X	0.1	2.0e-3×L_A
		Y	0.1	2.0e-2×B_A
		Z	0.1	1.5e-3×D_A
B	170 866	X	0.1	2.0e-3×L_B
		Y	0.1	2.0e-2×B_B
		Z	0.1	1.5e-3×D_B

Fig. 4 Visual of models with artificial noise, the port is original 3D points, the starboard is the noisy point cloud data ((a) Ship model A; (b) Ship model B)

Table 3 Comparison of compression performance

点云数量	原始点云	栅格化后点云	压缩率/%	时间/s	L_WL误差/%	B_WL误差/%
船模A	115 737	2 048 (32×64)	98.2	8.68	1.11	1.36
		8 192 (64×128)	92.9	9.95	1.23	1.84
		32 768 (128×256)	71.7	12.32	2.47	2.16
船模B	170 866	20 248 (32×64)	98.8	12.67	1.16	1.52
		8 192 (64×128)	95.2	13.91	1.38	1.91
		32 768 (128×256)	80.8	16.47	2.13	2.63

Fig. 5 Surface interpolation results for two samples (the port is the noisy point cloud-data, the starboard is the fitted surface) ((a) Ship model A; (b) Ship model B)

Fig. 6 Contour definition with offset values ((a) Offset matrix of normal contour; (b) Offset matrix of noisy contour)

Fig. 7 Contour correction and unfair area identification ((a) Ship model A; (b) Ship model B)

Fig. 8 Line odd and normal cases

Fig. 9 Sample A waterline before and after correction ((a) No. 0 waterline; (b) No. 31 waterline; (c) No. 63 waterline)

Fig. 10 Sample A station line before and after correction ((a) The No. 1 station line ; (b) The No. 32 station line; (c) The No. 94 station line; (d) The No. 126 station line)

Fig. 11 Grayscale comparison chart before and after sample correction ((a) Ship model A; (b) Ship model B)

Fig. 12 Key steps in the traditional FreeCAD modeling workflow ((a) Rough surface generated after point cloud skinning; (b) Final surface after contour fitting and redundant trimming)

Fig. 13 Comparison of surface correction and sectional area curves of ship model ((a) Ship model A; (b) Ship model B)

Table 4 Comparison between the original and modified models

参数	原始模型A	最终模型A	模型A的误差	原始模型B	最终模型B	模型B的误差
L_WL	61.96 m	62.66 m	1.13%	154.79 m	156.92 m	1.38%
B_W_L	10.82 m	11.11 m	2.68%	23.63 m	24.05 m	1.78%
Draft	4.12 m	4.13 m	0.24%	6.00 m	6.05 m	0.83%
Volume	1964.39 m³	1947.03 m³	-0.88%	13457.36 m³	13315.77 m³	-1.05%
C_B	0.711 2	0.677 2	-4.78%	0.613 2	0.583 2	-4.89%

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