Research on the method of 3D image reconstruction for cultural relics based on AR technology

doi:10.11996/JG.j.2095-302X.2025020369

Abstract

Abstract:

The surfaces of cultural relics possess complex and variable details, necessitating an exceptionally high level of precision in the data acquisition process to ensure the authenticity and accuracy of the reconstructed models. However, traditional methods often fail to achieve high precision while maintaining efficiency, and the subsequent processes of 3D model construction and texture mapping are highly complex, requiring substantial manual adjustments. To address these challenges, a method based on augmented reality (AR) was proposed for the digital three-dimensional image reconstruction of cultural relics. This method employed 3D scanning and high-resolution cameras to capture point cloud data and image data of the relics. The Scale-Invariant Feature Transform (SIFT) algorithm was utilized to extract feature points from both types of data. Based on these feature points, Revit software, in combination with SketchUp and Geomagic Studio, was used to construct the three-dimensional models of the cultural relics. Through virtual camera registration, the constructed 3D models were overlaid onto images of the actual relics, resulting in augmented virtual models. Subsequently, the real relics and the overlaid virtual models underwent virtual fusion, completing the digital three-dimensional image reconstruction of the cultural relics. Finally, by integrating virtual reality (VR) technology, interactive displays of the reconstructed digital 3D images were implemented. Experiments conducted using the Shimao cultural relics dataset as the experimental subject demonstrated that this method can accurately reconstruct digital 3D models of the relics. Comparative experiments indicated that the proposed method outperformed traditional approaches in both 3D reconstruction accuracy and texture mapping, while reducing the need for manual adjustments. This method effectively addressed the issues of insufficient precision and cumbersome operations inherent in traditional digital 3D image reconstruction, enabling efficient and accurate digital reconstruction of cultural relics. It provided a novel technological pathway for the preservation, virtual display, and digital archiving of cultural heritage.

Key words: augmented reality, 3D reconstruction, digitization of cultural relics, 3D registration, virtual real fusion

CLC Number:

TP391
K87

ZHOU Wei, CANG Minnan, CHENG Haozong. Research on the method of 3D image reconstruction for cultural relics based on AR technology[J]. Journal of Graphics, 2025, 46(2): 369-381.

Figures/Tables 12

Fig. 1 Extracting feature points of cultural relics based on SIFT algorithm

Table 1 Registration error results/mm

场景	场景描述	RMS 误差	Hausdorff 距离
场景1	具有多样几何形态的复杂表面雕刻物体	0.043	0.080
场景2	具有精细纹理的陶瓷物体	0.039	0.077
场景3	相对平滑的木雕艺术品	0.042	0.075
场景4	复杂表面纹理的岩画	0.040	0.079

Fig. 2 The framework for digital 3D image reconstruction of cultural relics

Fig. 3 The process of 3D registration using a virtual camera

Fig. 4 Schematic diagram of virtual and real fusion imaging

Table 2 Main parameters of the experiment

名称	数值
三维扫描仪	CR-Scan Ferret
扫描站	10
采样点密度	1.5 mm
测量距离	3~5 m
工业相机	JHEM131GC-L
采样帧频	30 fps
LED灯	YMFB-7
数据采集卡	NIPCI/PXI-662
计算机	ADVANTECH-MIC-7700
VR平台	SteamVR

Fig. 5 Cultural relic data collection platform

Table 3 The model reconstructed by the method in this paper is used to evaluate the geometric differences

场景	名称	RMS误差/mm	Hausdorff距离/mm
简单几何形态	玉琮	0.035	0.052
简单几何形态	铜尺环	0.028	0.048
复杂几何形态	11号石雕(横)	0.041	0.061
复杂几何形态	立柱石雕(竖)	0.030	0.050
多种材质	鱼形佩	0.027	0.045
	绿松石串	0.032	0.053
	玉人头像	0.033	0.055
	玉环	0.036	0.059
	手串	0.034	0.057
	青铜臂钏	0.031	0.051
	陶鹰	0.030	0.052
	口簧	0.029	0.061

Fig. 6 The digital 3D image reconstruction of cultural relic entities using the method in this article combined with virtual reality interactive results display ((a) Virtual platform; (b) Touch interface; (c) Voice introduction; (d) Rotatable and scalable model)

Fig. 7 Performance test of the proposed feature point extraction method

Fig. 8 Virtual and real fusion performance test ((a) Geometric consistency; (b) Illumination consistency; (c) 3D error distribution; (d) Comprehensive effect)

Table 4 Comparison results of the accuracy and efficiency of this article’s method with the other three methods in different scenarios

方法	RMS误差/mm			Hausdorff 距离/mm	平均计算时间/s
方法	场景1	场景2	场景3	Hausdorff 距离/mm	平均计算时间/s
本文	0.040	0.045	0.043	0.08	120
NeRF	0.065	0.075	0.070	0.10	180
MVS	0.055	0.065	0.060	0.09	150
ICP	0.080	0.085	0.078	0.12	90

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