Tech Term Decoded: NeRF (Neural Radiance Fields)

Definition

NeRF, an acronym for Neural Radiance Fields, is a deep learning technique that processes some 2D images of a scene and produces a realistic 3D model. This allows viewing from any angle, including angles missing in the original photos. Think of NeRF as giving AI-powered eyes to a machine. It doesn’t just see the images, it understands how light and geometry work together to create a 3D world [1].

For instance, imagine you visited the mystical Ogbunike Caves complex in Anambra and took 7-9 photos during your guided spelunking adventure through the interconnected limestone caverns and underground passages. Using NeRF technology, you could transform these limited cave exploration photos into a comprehensive 3D model of the entire subterranean network.

This NeRF application could similarly recreate the Awhum Waterfall and Cave system in Enugu, the underground river systems of Kwara's Owu Falls, or Cross River's ancient cave paintings for geological research and virtual adventure tourism.

 

An example of NeRF producing a 3D scene from several input views [2].

Origin

In other to solve the challenges of traditional 3D rendering methods, researchers at Google Research developed NeRF. In March 2020, a NeRF paper about this novel approach was released, describing how a Multilayer Perceptron, a fully connected deep network, could be used to model the volumetric scene, including how it can drastically reduce computation requirements and improve 3D structure quality [3].

Context and Usage

NeRF is already been used in various applications, and its potential for future applications is vast. NeRF technology is bridging the gap between 2D images and interactive 3D experiences.

• E-commerce and Retail: NeRF improves shopping experience of customers, via it being used to create interactive 3D models of products, enabling customers to view items from different angle online.
• 3D Scene Reconstruction: NeRFs is effective at producing digital replicas of real-world environments and objects. This can be seen in the case of Google Maps' "Immersive View", which uses NeRFs to build detailed, interactive 3D models of cities. This has applications in urban planning, virtual tourism, and cultural heritage preservation.
• Robotics and Autonomous Systems: Autonomous vehicles and robots require 3D environmental understanding for effective navigation and interaction. NeRFs can provide a rich, detailed 3D map from sensor data, improving a robot's ability to perceive its surroundings.
• Entertainment and Visual Effects (VFX): The ability to generate photorealistic views is invaluable in filmmaking and video games. NeRFs can be used to create realistic virtual sets, digitize actors, and generate complex visual effects that are difficult to achieve with traditional methods. Companies like Luma AI are developing tools to make this technology more accessible [4].

Why it Matters

NeRFs demonstrate exceptional potential in representing 3D data more efficiently than other techniques and may enable new ways to produce highly realistic 3D objects automatically. Time magazine called a NeRF implementation from the Silicon Valley chipmaker Nvidia one of the top inventions of 2022. Alexander Keller, director of research at Nvidia, told Time that NeRFs "could ultimately be as important to 3D graphics as digital cameras have been to modern photography." Used with other techniques, NeRFs have the potential for massively compressing 3D representations of the world from gigabytes to tens of megabytes.

In Practice

Google is a good example of a real-life case study of NeRF in practice. Google has already started using NeRFs to translate street map imagery into immersive views in Google Maps, building detailed, interactive 3D models of cities. This has applications in urban planning, virtual tourism, and cultural heritage preservation [4].

See Also

Related AI Models and Architectures:

• Neural Network: Computing system inspired by biological neural networks that learns patterns from data
• RoBERTa: Robustly Optimized BERT Pretraining Approach, an improved transformer language model

Reference

1. Pandya, K. (2025). Neural Radiance Fields (NeRF) — Turning 2D Images into 3D Scenes.
2. Lawton, G. (2025). What is neural radiance field (NeRF)?
3. Cleveland, J. (2023). Overview of Neural Radiance Fields (NeRF).
4. Ultralytics. (2025). Neural Radiance Fields (NeRF).