3D Reconstruction Technology of Virtual Art Museum Integrating Neural Radiation Field
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Abstract
High-fidelity three-dimensional reconstruction of virtual art museums is essential for digital cultural heritage preservation and immersive visual communication, while also providing valuable references for computational imaging and electromagnetic-based scene perception under complex radiance propagation conditions. However, conventional Neural Radiance Field (NeRF) methods are limited by unstable training and texture distortion caused by specular reflections, translucent materials, and non-uniform illumination, making it difficult to preserve fine artistic details. To address these challenges, this study proposes a Distortion-Aware Ray Sampling Neural Radiance Field (DARS-NeRF) framework that integrates distortion-aware ray sampling with structure–material–lighting decoupled modeling. The proposed method enhances sampling density in visually distorted regions while combining Mask2Former semantic segmentation and a Light Estimation Network to provide semantic priors and illumination constraints for physically consistent reconstruction. Experiments conducted on 14,700 real-world art gallery images demonstrate that DARS-NeRF achieves PSNR, SSIM, and LPIPS values of 28.9 dB, 0.912, and 0.142, respectively, outperforming the strongest baseline by 2.2 dB, 3.2%, and 16.0%. In addition, Albedo MAE and Roughness RMSE are reduced by 18.3% and 20.4%, while View Consistency reaches 6.42 lux, indicating superior robustness to illumination variations. The proposed framework enables collaborative reconstruction of geometry, materials, and lighting with enhanced physical consistency, providing an effective paradigm for virtual museums, digital preservation, remote education, and high-fidelity intelligent visual sensing.
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