The past few years have seen huge leaps in 3D modeling and visualization, thanks in part to techniques such as Gaussian Splatting and Neural Radiance Fields (NeRFs). Both methods have significantly changed the way we capture and view 3D environments. Yet many applications still use traditional techniques such as photogrammetry. Photogrammetry has the advantage that it can provide precise metric data and is relatively widely applicable, especially when accurate dimensions are essential.
Whereas photogrammetry often relies on complex manual processes and static point clouds, NeRFs and Gaussian Splatting offer a smoother and more realistic representation with less effort. But what really are the differences between these two new techniques? And can we perhaps use them in the future to digitize real estate, for example, with detailed 3D models, even though we cannot currently do precise measurements with them? In this blog, I explain.
Neural Radiance Fields, or NeRFs, are a new technique that allows you to create a 3D representation of a scene based on a few ordinary 2D photographs. NeRFs use deep learning to learn how light moves through a room, including how it is reflected. This allows the system to create a realistic 3D visualization of the photographed environment.
How does NeRF work? It starts with a collection of photographs of a scene from different angles. By looking at the color and light intensity of each pixel, the system learns the underlying 3D structure of the room. For example, NeRF can render a living room or an outdoor space as a three-dimensional scene, complete with details such as shadow and depth.
Gaussian Splatting is another approach to creating 3D models that is particularly interesting for its speed and simplicity. Instead of building a scene out of hard, single points as in traditional point clouds, Gaussian Splatting uses Gaussian "spots" to make the space between points more fluid and natural.
How does Gaussian Splatting work? Instead of calculating with fixed points in space, Gaussian Splatting spreads each point out a bit, giving you a smoother 3D model. This gives a nice smooth result that is especially useful for objects with complex or detailed shapes, such as a building or interior. Moreover, Gaussian Splatting requires relatively little computing power, making it a cost-effective solution for creating realistic 3D models without expensive hardware or extensive processing power.
With these techniques developing so rapidly, the question arises whether they could also be useful for sectors such as real estate. Imagine being able to create a complete 3D model of a house or office with just a few photos, allowing potential buyers or tenants to virtually explore the building from different angles without being physically present.
NeRFs and Gaussian Splatting offer interesting possibilities in this context. They can enable real estate agents and clients to create realistic virtual tours for individual buildings or even entire neighborhoods. This opens the door to a more efficient and accessible way to present and experience real estate.
Size limitations One disadvantage of both techniques is that they do not give exact measurements. In real estate, it is essential to have precise measurements for things such as the area of a house or the height of a ceiling. NeRFs and Gaussian Splatting offer visually impressive results, but cannot be used to generate accurate metric data.
Despite these limitations, there are many opportunities to further develop these techniques. Researchers are working to improve the accuracy of 3D models so that they may be able to provide measurements in the future. For example, consider combining NeRFs with lidar technology, which does have the ability to measure very accurate distances. This could eventually give us systems that generate both realistic 3D views and precise measurement data.
In addition, we see that computers are becoming more powerful and AI algorithms continue to improve. This may allow us to digitize entire cities or neighborhoods in the future with NeRFs or Gaussian Splatting. This would allow real estate companies to offer their properties digitally in a way that is both impressive and informative.
Gaussian Splatting and Neural Radiance Fields represent an exciting future for 3D modeling. Although they are not yet suitable for applications that require precise dimensions, such as in real estate, they do offer a promising prospect for detailed and visually compelling 3D reconstructions. As these technologies are further developed, they may play an increasingly important role in how we digitally capture and visualize buildings, objects and even entire cities.
At SupRmen, we follow these developments closely. Once the quality and functionality of these technologies are sufficient, we can incorporate them into our services and products. This keeps us at the forefront of providing innovative and valuable solutions for our customers.
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