Gaussian Splats and NeRF vs Mesh Based Rendering for Real-Time Simulation

With all the excitement around NeRF and Gaussian Splats, in the realm of real-time 3D simulation and dynamic synthetic data, different rendering technologies offer unique advantages tailored to specific applications. Here, we compare the strengths of NeRF (Neural Radiance Fields) and Gaussian Splatting with traditional game engine mesh rendering to highlight where each method excels and where it might fall short.

Advantages of NeRF and Gaussian Splatting

Photorealistic Static Scenes: NeRF and Gaussian Splatting shine in creating photorealistic renderings of static scenes. NeRF, with its capacity to produce highly detailed and complex scenes from a series of images, is particularly suitable for applications like virtual tours or high-resolution digital museums, where dynamic interaction is minimal.

Gaussian Splatting also excels in visualizing volumetric effects and complex textures, making it ideal for visual effects in movies or detailed architectural visualizations.

Complex Geometries Handling: NeRF is adept at capturing complex geometries that traditional mesh-based methods may not render as effectively, such as intricate patterns and fine details like foliage and fabrics. This makes it valuable for scientific visualization or any application where accuracy in visualizing detailed static objects is crucial.

Controlled Lighting and Viewpoints: Both techniques are highly effective in environments with controlled lighting and static viewpoints. They can precompute lighting effects and reflections, which are unchangeable post-render, offering a level of realism hard to match in real-time environments where such factors are variable.

Advantages of Traditional Game Engine Mesh Rendering

Dynamic Interactions: Traditional mesh rendering in game engines is optimized for dynamic interactions and real-time modifications. Game engines are designed to handle changes in geometry, lighting, and materials on the fly, making them the go-to choice for video games and interactive simulations where user input affects the environment.

Real-Time Performance: Mesh-based rendering is highly optimized for performance, with dedicated hardware support like GPUs tailored to process large amounts of polygonal data quickly. This is essential for maintaining the high frame rates required for a smooth real-time experience in games and VR.

(There are advances here coming quick, like this approach https://arxiv.org/html/2403.13806v1)

Animation and Rigging: Traditional rendering supports complex animations and character rigging, allowing for real-time skeletal animations, facial expressions, and other dynamic movements that NeRF and Gaussian Splatting currently cannot manage without significant preprocessing or computational overhead.

(There are advances in animation in GS but still highly limited: https://zerg-overmind.github.io/GaussianFlow.github.io/)

Comprehensive Material and Lighting Features: Game engines support a wide array of dynamic lighting and material effects, including shadows, reflections, refractions, and more sophisticated effects like subsurface scattering. These features are crucial for creating immersive and responsive environments that react to player actions and changing conditions.

Non-Visual Simulation: Incorporating non-visual elements into simulation like other types of sensor data like IMUs.

Conclusion

While NeRF and Gaussian Splatting offer unmatched realism for static scenes and complex geometries, they currently face significant limitations in dynamic and interactive environments due to their computational demands and static nature.

On the other hand, traditional game engine mesh rendering, though potentially less capable of achieving the same level of detail in certain static scenes, remains the superior choice for real-time simulation and gaming due to its versatility, performance, and support for dynamic content. Each technology thus has its place, with the choice depending on the specific needs and constraints of the project at hand.

There are people trying to push the envelope (https://yingjiang96.github.io/VR-GS/ and https://zerg-overmind.github.io/GaussianFlow.github.io/) and it'll be exciting to see this all come together.

Here's a great resource of all things Gaussian Splatting:

https://github.com/MrNeRF/awesome-3D-gaussian-splatting?tab=readme-ov-file#dynamics-and-deformation