Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of a higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in that they recreate 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used, namely, a building and a university campus. As a result, 3D Gaussian data have proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twins.

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D, and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in recreating 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used including a building and a university campus. As the result, 3D Gaussian data has proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twin.

Traditional holographic printing techniques prove inadequate when only input data are available. Therefore, this paper proposes a new artificial-intelligence-based process for generating digital holographic stereograms from a single black-and-white photograph. This method eliminates the need for stereo cameras, photogrammetry, or 3D models. In this approach, a convolutional neural network and deep convolutional neural field model are used for image colorization and a depth-map estimation, respectively. Subsequently, the colored image and depth map are used to generate the multiview images required for creating holographic stereograms. This method efficiently preserves the visual characteristics of the original black-and-white images in the final digital holographic portraits. This provides a new and accessible method for holographic reconstruction using limited data, enabling the generation of 3D holographic content from existing images. Experiments were conducted using black-and-photographs of two historical figures, and highly realistic holograms were obtained successfully. This study has significant implications for cultural preservation, personal archiving, and the generation of life-like holographic images with minimal input data. By bridging the gap between historical photographic sources and modern holographic techniques, our approach opens up new possibilities for memory preservation and visual storytelling.

Traditional holographic printing techniques prove inadequate when only input data is available. Therefore, this paper proposes a new artificial-intelligence-based process for generating digital holographic stereograms from a single black-and-white photograph. This method eliminates the need for stereo cameras, photogrammetry, or 3D models. In this approach, a convolutional neural network and deep convolutional neural field model are used for image colorization and a depth-map estimation, respectively. Subsequently, the colored image and depth map are used to generate the multi-view images required for creating holographic stereograms. This method efficiently preserves the visual characteristics of the original black-and-white images in the final digital holographic portraits. This provides a new and accessible method for holographic reconstruction using limited data, enabling the generation of 3D holographic content from existing images. Experiments were conducted using black-and-photographs of two historical figures and highly realistic holograms were obtained successfully. This study has significant implications for cultural preservation, personal archiving, and the generation of life-like holographic images with minimal input data. By bridging the gap between historical photographic sources and modern holographic techniques, our approach opens up new possibilities for memory preservation and visual storytelling.

Holographic stereograms record large sets of multi-view projection images into elementary hologram units (hogels), and their visual quality is governed primarily by the quality and inter-view consistency of the input multi-view imagery. Conventional photogrammetry-based pipelines for generating such imagery rely on surface meshes and require burdensome post-processing, which limits the fidelity of thin structures and view-dependent reflections. This paper proposes an integrated workflow that replaces the photogrammetric stage with Gaussian splatting and presents a controlled comparison of volumetric 3D Gaussian splatting (3DGS) and surface-aligned 2D Gaussian splatting (2DGS) for hogel-based digital hologram production. Using 397 drone-captured images of a 3 m bronze Pegasus statue, both representations were trained under identical input data, camera poses, and hyperparameters, and 119,808 full-parallax multi-view images (768 × 156 grid; 120° × 52° field of view) were rendered from each model, converted into hogel arrays by an identical ray-tracing transform, and printed onto Ultimate U04 silver halide plates under identical optical conditions. In the digital domain, 3DGS outperformed 2DGS on all three standard novel-view-synthesis metrics (PSNR 32.68 vs. 31.03 dB; SSIM 0.9281 vs. 0.9139; and LPIPS 0.1150 vs. 0.1406) while using approximately 1.67 times more Gaussians. Conversely, 2DGS produced superior results at viewpoints outside the training distribution, on thin structures such as wings and mane, and at the extremes (±60°) of the virtual camera array, and these differences propagated consistently to the printed holograms, whose edge sharpness was higher for 2DGS at every measured viewpoint (mean 39.6 vs. 23.8). Notably, 2DGS achieved this superior printed-output quality while using approximately 40% fewer Gaussians than 3DGS, combining model efficiency with the multi-view consistency that proved decisive for hogel-based printing. The results demonstrate that single-view metrics such as PSNR do not capture the multi-view consistency that dominates hogel-based output quality and provide practical guidance for representation selection in holographic-printing workflows.