3D Gaussian Splatting Metaverse Space Generation Service
What is Gaussian Splatting?
Gaussian Splatting is a technology that represents three-dimensional objects by scattering “colored clouds” throughout a space, unlike conventional 3D data composed of collections of “points.” These “clouds” are not merely points; they contain information such as position, orientation, spread, and color blending, and are rendered so that each one blends naturally into the space. As a result, smooth 3D representation without gaps becomes possible, reproducing realistic textures as if a real-world scene had been captured as it is. With conventional point clouds, a large number of points are required to achieve a realistic appearance. In contrast, Gaussian Splatting can produce high-quality rendering even with a relatively small number of points. Because natural depth is created through the overlap of calculated “clouds,” the display data can be kept lighter, contributing to more efficient processing. This cutting-edge 3D representation technology is attracting attention not only for the visualization of architecture, civil engineering structures, and cultural assets, but also in a wide range of business fields where “visual presentation” has value, such as VR, video production, and web-based display.
Example of 3D Space Generation — Video Demo
This is an example of a 3D bridge space model generated by our company using a 360° camera and Gaussian Splatting. The structure can be represented realistically, and even areas that are difficult to express in drawings can be reproduced in detail, making the model useful for purposes such as planning on-site inspection surveys.
Comparison Between Point Clouds and Gaussian Splatting
Point clouds have long been used for surveying and 3D recording. While they are easy to acquire and manage, their visual quality tends to depend heavily on point density, and there are limitations in achieving smooth and realistic representation. Gaussian Splatting, on the other hand, is an advanced visualization method that has emerged in recent years. By treating each point not merely as positional information but as an “information unit spreading through space,” it enables highly accurate and realistic 3D representation.
| Item | Point Cloud | Gaussian Splatting |
| Representation Method | Represents the shape of an object using a large number of points placed in 3D space | Assigns each point not only position, but also attributes such as orientation, spread, and color distribution, enabling rendering with depth and continuity |
| Appearance | May appear rough, with gaps that tend to stand out | High-density, realistic texture Photorealistic appearance |
| Data Acquisition Method | Mainly laser scanning, photogrammetry, and similar methods | Reconstructed based on captured image data (Analysis of parallax and overlap between images) |
| Data Volume | Increases according to the number of points, and storage costs and post-processing workload can also become significant | Can be made lighter because smooth rendering is possible even with lower point density |
| Main Applications | 3D data for surveying, design, and record keeping | Used for high-quality visual expression, such as video, VR, and web-based display |