Enhancing cultural heritage digitalization through 3D graphics algorithm and immersive visual communication technology

Fang Yuan — Mon, 16 Feb 2026 00:00:00 +0000

With the continuous advancement of digital technology, cultural and creative product design is shifting from static presentation to dynamic immersive experience. The research aims to address the challenges faced by traditional modeling methods in accurately restoring complex textures and cross platform visual communication. The neural radiation field algorithm was enhanced by introducing a multi-level cost volume fusion module and a Gaussian uniform mixture sampling strategy. Furthermore, a collaborative visual communication framework integrating augmented reality and virtual reality was constructed, achieving a transition from single image input to high-precision 3D reconstruction, and then to dynamic interaction. The experiment showed that the improved algorithm achieved peak signal-to-noise ratios of 30.63 and 30.15 on the UoM-Culture3D and Bootstrap 3D synthetic datasets, respectively, with structural similarity indices of 0.88 and 0.89, respectively. Field deployment tests have shown that integrating AR and VR technologies into visual communication strategies significantly improves spatial perception consistency, prolongs user engagement time, and enhances detail recognition accuracy. This research emphasizes the potential of combining deeply coupled 3D graphics algorithms with immersive technology, which can help improve the digital restoration accuracy and cultural dissemination efficiency of cultural and creative products, thereby supporting the modern inheritance of traditional culture.

Intelligent extraction and layout optimization of digital media visual elements based on computer vision

Hebin Wu — Sat, 21 Feb 2026 00:00:00 +0000

In the field of digital media, intelligent extraction and layout optimization of visual elements face challenges such as inaccurate semantic understanding of elements and low efficiency in generating layout strategies. This study proposes an extraction and layout optimization model that integrates visual semantic understanding with intelligent optimization strategies, based on a segmentation Vision Transformer and Multi-Objective Firefly Algorithm. The model also utilizes the improved optical flow methods to efficiently capture dynamic information during the design process. Experimental results show that the segmentation Vision Transformer algorithm achieves an extraction accuracy of 98.8±0.2% for different categories of visual elements. As the training progresses to 50 iterations, the average Intersection-Over-Union stabilizes at 0.95, and the harmonic mean of recall reaches 98.17±0.38\%. The evaluation of the integrated model shows that it achieves 99% accuracy in extracting visually similar elements. After layout optimization using the model, the aesthetic score increases to 95.6, and the spatial occupancy rate improves to 97.2%. The above results indicate that the model proposed by the research institute can effectively enhance the accuracy of visual element extraction and the quality of layout optimization, significantly reducing the reliance of traditional methods on manual rules, and providing an efficient and adaptive solution for the automated design of digital media.

Machine Graphics & Vision

Enhancing cultural heritage digitalization through 3D graphics algorithm and immersive visual communication technology

Intelligent extraction and layout optimization of digital media visual elements based on computer vision