A neural radiance field (NeRF) is a neural network that can reconstruct complex three-dimensional scenes from a partial set of two-dimensional images. Three-dimensional images are required in various simulations, gaming, media, and Internet of Things (IoT) applications to make digital interactions more realistic and accurate. The NeRF learns the scene geometry, objects, and angles of a particular scene. Then it renders photorealistic 3D views from novel viewpoints, automatically generating synthetic data to fill in gaps.
What are the use cases of neural radiance fields?
NeRFs can render complex scenes and generate images for various use cases.
Computer graphics and animation
In computer graphics, you can use NeRFs to create realistic visual effects, simulations, and scenes. NeRFs capture, render, and project lifelike environments, characters, and other imagery. NeRFs are commonly used to improve video-game graphics and VX film animation.
Medical imaging
NeRFs facilitate the creation of comprehensive anatomical structures from 2D scans such as MRIs. Their technology can reconstruct realistic representations of body tissue and organs, giving doctors and medical technicians useful visual context.
Virtual reality
NeRFs are a vital technology in virtual reality and augmented reality simulations. Because they can accurately model 3D scenes, they facilitate creating and exploring realistic virtual environments. Depending on your viewing direction, the NeRF can display new visual information and even render virtual objects in a real space.
Satellite imagery and planning
Satellite imagery provides a range of images that NeRFs can use to produce comprehensive models of the earth’s surface. It is useful for reality capture (RC) use cases that require digitizing real-world environments—you can transform spatial location data into highly detailed 3D models. For example, the reconstruction of aerial imagery into landscape renders is commonly used in urban planning because it gives a useful reference for the real-world layout of an area.
The following image shows an example of a 3D building model.
How do neural radiance fields work?
Computer-rendered 3D images, created through various digital graphics techniques, possess several distinct properties that define their quality and realism. For example:
- Geometric aspects, such as positioning, orientation, and scale of 3D models in the scene
- Lighting aspects, such as shadows, brightness, color, and reflections
- Transparency and translucency that show how light passes through materials like glass or fog
- Volume and density, such as the density of smoke or clouds
- Textures simulating materials like cloth, wood, or metal
The choice of colors and their distribution also play a key role in the image’s visual impact. Shading determines how different surface areas are illuminated, creating a sense of depth and form.
NeRFs combine computer graphics techniques with neural network architecture to handle all the preceding aspects.
The following image is an example of a computer-rendered 3D image.
Architecture of neural radiance fields
NeRFs use a neural aspect called multilayer perceptron (MLP)—a fully connected neural network architecture—to create a representation of a 3D scene. An MLP is a foundational model in neural networks and deep learning. It’s trained to map spatial coordinates and viewing directions to color and density values. An MLP uses a series of mathematical structures that organize inputs—such as a position in 3D space or a 2D viewing direction—to determine the color and density values at each point in a 3D image.
The network also learns how to alter the brightness and color of light rays in the scene. By developing an understanding of these rays, known as radiance modeling, it can display different colors and densities from different perspectives.
Types of neural radiance fields
The earliest versions of NeRFs were difficult to optimize and slow, and they needed input from photos to match lighting using the same camera rays. Since then, several variations have improved on the initial technology.
PixelNeRF
One of the earliest new forms of NeRF was the PixelNeRF (CPVR 2021). This deployment introduces a fully convolutional architecture that can condition on single image inputs. This approach removes the need for many calibrated and organized views and reduces the total computational resources needed. This new approach streamlined the process of creating and optimizing NeRFs.
Mega-NeRD
Mega-NeRD (CVPR 2022) is another NeRF framework that is especially useful when working with large-scale scenes. It offers an alternative geometric clustering algorithm and sparse network structure that enable it to process images with diverse lighting conditions. This optimized NeRF uses a sparse neural radiance grid (SNeRG) to capture and render real-world environments effectively.
NSVF
Neural Sparse Voxel Fields (NSVF) is a NeRF that can skip any empty pixels during its rendering phase, which increases rendering speed. This technique learns pixel structures in network cells, enabling it to create high-quality images without needing specific viewpoints.
Plenoptic voxel
Instead of using the multilayer perceptron neural network, the Plenoptic Voxel (2021) innovation uses a sparse 3D grid. With this grid, you can increase the rendering speed of new simulations while maintaining visual fidelity through voxel interpolation.
How do neural radiance fields render images?
Neural rendering is the term for the process of image creation by the NeRF. NeRFs use various techniques to transform raw data into detailed 3D representations.
Graphics rendering
Rendering techniques from the field of computer graphics enable NeRF models to project and manipulate scenes geometrically. For example:
- Ray casting simulates the user’s perspective to compute the visibility of objects
- Ray tracing extends this concept by simulating the physical behavior of light, including reflection, refraction, and shadows
- Rasterization converts 3D vector information into pixels on a 2D screen, employing various algorithms to simulate lighting and texture effects efficiently
Volume rendering
Volume rendering is another essential strategy. An algorithm determines the red, green, blue, and alpha (volume density) values for each pixel in a 3D space or image. These figures map to their corresponding positions in a 2D image.
Another technique is view synthesis, which operates inversely to volume rendering. This technique constructs a 3D view from a series of 2D images. View synthesis creates a hemispheric layout that represents the object using a range of images taken from different angles. This process fits each 2D image to its respective position around an object, reconstructing it in 3D.
