Introduction to Radiance Fields

This module is currently in development.  Radiance field products and technology is moving extremely quickly right now and a number of them have introduced critical features which makes them more useful.  As such, this module development will have some basic lessons then follow up with more as new products and technology emerges.

This module introduces cutting edge techniques for photorealistic capture employing advanced artificial intelligence techniques.  The integration of NeRFs and Gaussian splatting into 3D scanning workflows represents a significant advancement in the field. These technologies provide unprecedented levels of detail and realism, enhancing the capabilities of 3D scanning for games and visual effects. With the availability of powerful software tools and user-friendly smartphone apps, both professionals and hobbyists can explore and utilize these cutting-edge techniques in their projects.

NeRFs and Splats Summaries and Differences

NeRF (Neural Radiance Fields): NeRFs are an innovative technology that utilizes neural networks to synthesize novel views of complex 3D scenes from 2D images. By learning to predict color and density values at any point in space, NeRFs can render highly detailed and realistic scenes. This makes them particularly useful in applications where capturing intricate details and realistic lighting effects are crucial, such as in game development, visual effects, and virtual reality environments. NeRFs focus on high-quality, photorealistic rendering through neural network-based volumetric representations, ideal for detailed and static scene reconstructions.

Gaussian Splatting: Gaussian Splatting is a technique for representing 3D surfaces using overlapping Gaussian functions. This method captures fine details and complex geometries efficiently, making it suitable for real-time applications. Gaussian splats offer a compact and flexible representation that can handle dynamic scenes and is useful in scenarios requiring fast and efficient rendering, such as interactive visualizations and real-time simulations.

Differences: In essence, NeRFs offer superior quality and generalization for novel views, while Gaussian Splats provide efficiency and speed.  Splat quality is improving dramatically and is showing to be comprehensively supported across multiple domains.  It is safe to say, at least for now gaussian splats will be the primary technology moving forward, at least in the next 12 months.

Datasets

There are numerous radiance field datasets available online with one of the most notable from Facebook Research. VR-NeRF: High-Fidelity Virtualized Walkable Spaces