Imagine trying to model a city’s flood risk using a 3D visualization technique that can’t accurately represent terrain slopes. This is one of the challenges of Gaussian splatting in GIS applications. While Gaussian splatting offers some impressive visual capabilities, its limitations become apparent when precision and accurate geometric representation are crucial. In industries like Geographic Information Systems (GIS) and Building Information Modeling (BIM), the ability to perform detailed spatial analysis, create editable models, and interact with standardized formats is essential. That’s where 3D meshes come in—offering structured surfaces and precise geometry that Gaussian splatting simply can’t match. But does this mean that Gaussian splatting has no place in these industries? In this post, we’ll explore the limitations of Gaussian splats compared to traditional 3D meshes and when each might be most useful for GIS and BIM applications.
One of the most significant differences between Gaussian splatting and 3D meshes is that splats lack an explicit geometric structure. Gaussian splats represent 3D data as a collection of points with Gaussian distributions, often rendering surfaces in a continuous, volumetric manner. This is ideal for applications like point cloud visualization or immersive environments where the focus is on visual representation.
On the other hand, 3D meshes are composed of vertices, edges, and faces, offering a clear, structured surface representation. This structure is crucial in GIS and BIM applications, where the geometry of buildings, roads, and terrain must be defined with precision for accurate spatial analysis. Unlike Gaussian splats, meshes support well-defined surfaces that allow for topological operations like intersections, buffering, and terrain modeling—operations that are foundational in GIS.
GIS workflows often require detailed and accurate geometric representations to perform spatial analysis. For instance, operations such as line-of-sight analysis, flood modeling, or terrain slope calculations require clearly defined surfaces to generate accurate results.
Gaussian splats, due to their lack of a rigid surface definition, pose challenges in these areas. While they can produce visually appealing representations, their lack of an explicit geometry makes it difficult to apply traditional GIS algorithms effectively. 3D meshes, with their structured polygons, are better suited for these tasks. They provide precise surfaces that GIS tools can manipulate for sophisticated spatial analysis, which is vital for planning, risk assessment, and infrastructure development.
For GIS and BIM professionals, interoperability with existing software is crucial. Tools like ArcGIS, Revit, AutoCAD, and QGIS are built around polygonal 3D models (meshes) and support industry-standard formats such as OBJ, FBX, and STL. These meshes can be edited, manipulated, and analyzed with the click of a button in most professional-grade software.
Conversely, Gaussian splats are still relatively new to the scene and lack widespread support in these platforms. Users may need custom solutions or additional plugins to visualize or convert Gaussian splats into usable formats. This makes integrating Gaussian splatting into existing GIS and BIM pipelines cumbersome and inefficient. The established ecosystem of 3D mesh workflows is far more streamlined for professionals working on large-scale infrastructure or city planning projects.
In BIM applications, editing and modifying 3D models is a frequent task. Whether it’s updating the design of a building, adjusting the topography, or changing the layout of utilities, precision is key. 3D meshes allow for detailed editing using tools within BIM software, making them ideal for architectural design and modification.
Gaussian splatting, however, is primarily a rendering technique. It excels in visualizing pre-existing data but struggles when it comes to precise edits. Gaussian primitives are often treated as a collection of points without clear edges or faces, making it difficult to perform edits like scaling, reshaping, or modeling new structures. This makes Gaussian splats less suited for BIM applications, which require constant model updates and changes.
One of the advantages of Gaussian splatting is its ability to render large datasets in real time. By using splats instead of traditional meshes, the rendering engine can achieve photorealistic effects with significantly less computational cost. However, when it comes to large-scale GIS applications, such as mapping entire cities or analyzing large-scale terrains, this can be a double-edged sword.
3D meshes can be optimized using Level of Detail (LOD) techniques to reduce the computational load in areas of less importance while retaining detail in critical areas. This makes them more suitable for large-scale applications like urban planning or environmental monitoring. In contrast, Gaussian splats require significant GPU power and memory to render complex scenes, and their efficiency diminishes as the scale and detail of the model increase.
Another limitation of Gaussian splats is their lighting and material representation. Gaussian splats rely on appearance-based techniques, often using baked lighting, which can look unrealistic in dynamic environments. While this may be fine for quick visualizations or certain gaming applications, it does not provide the level of physical realism required for BIM simulations like energy efficiency modeling or real-world material behavior under various lighting conditions.
3D meshes, however, are compatible with physically-based rendering (PBR) techniques, allowing for realistic simulations of materials, lighting, and shadows. This is particularly important in BIM applications where accurate material representation is needed to model real-world scenarios like energy consumption, structural integrity, and daylighting analysis.
While Gaussian splatting is still relatively new and not yet mainstream in GIS and BIM, it does have its place in specific use cases. It is ideal for real-time visualization, immersive experiences, or rapid 3D reconstruction from point clouds and imagery. For instance, it could be used in urban visualizations or metaverse applications where photorealistic rendering speed is crucial.
However, for traditional GIS and BIM workflows—where precise geometry, spatial analysis, editing, and interoperability are essential—3D meshes remain the standard. They are well-established, widely supported, and can be used for advanced analytical tasks and accurate modeling, making them the better choice for most applications in the geospatial and construction industries.
As the technology matures, it is possible that a hybrid approach—combining the rendering efficiency of Gaussian splats with the precision of 3D meshes—could bridge the gap, offering the best of both worlds. Until then, 3D meshes will likely remain the gold standard for GIS and BIM professionals.
What’s your experience with Gaussian splatting or 3D meshes? Share your thoughts in the comments below!