What is NeRF and Why it is very important!

4 min readAug 28


What is NeRF!

Have you ever wondered how to turn your 2D photos into a digital 3D scene? With the latest advances in artificial intelligence, you can now do it in a matter of seconds. In this post, I hope you will know more about NeRF, a neural rendering technique that can generate novel views of complex 3D scenes, based on a partial set of 2D images.

NeRF stands for Neural Radiance Field, which is a fully-connected neural network. NeRF learns to represent and render realistic 3D scenes by optimizing a continuous volumetric scene function using a rendering loss that reproduces input views of a scene.

To use NeRF, you need to provide a sparse set of input views that capture a scene from different angles. NeRF then trains a neural network to predict the colour and opacity of any point in the scene, given its location and viewing direction. By sampling many points along each viewing ray, NeRF can synthesize novel views of the scene using volume rendering.

Image Source

NeRF can produce high-quality results that are indistinguishable from real images, even for scenes with complex geometry and lighting.

If you want to learn more about NeRF and how it works, you can check out this 2020 research paper on NeRF, by researchers from UC Berkeley, UC San Diego, and Google Research.

NeRF limitless possibilities:

After generating the 3D scene with NeRF, we can create any camera motion path (I mean virtually) that would both loop perfectly and allow us to create the many different versions of the video we want to render, this is because the camera is created, placed, and controlled mathematically in post-production. That’s why we can easily create looping videos. Even with the best motion control gear, this kind of perfect loop is hard to achieve.

Add virtual cameras to the 3D scene
Adjust the time intervals of the virtual cameras

During a regular film shooting, for Commercial advertisements for example, sometimes we can’t get a smooth video like we wish to have, since you only get the frames you shoot and finish in post processing, and also that’s going to be limited by the time frame you have and budget you have. With NeRFs that’s not the case, because your generated video is rendered from a virtual scene, and the length of the video isn’t in any way restrained by the available video frames.

This means it’s always possible to create multiple different versions of the video at different aspect ratios, and the results will never suffer. For example from the same original video footage (or 2D images set), we can generate a 1:1 aspect ratio video for our social network feed, another 9:16 video for our social network story and 16:9 for our main video.

Set the aspect ratio of the video

NeRF Applications:

NeRF is a powerful tool for creating synthetic data, which can be used for various applications:

Robotics: NeRF can be used to train robots and self-driving cars to understand the size and shape of real-world objects by capturing 2D images or video footage of them.

Urban mapping: NeRF can be used to create 3D maps of cities or landscapes from aerial or satellite images, which can help with planning, navigation, or tourism.

Virtual reality/augmented reality: NeRF can be used to create immersive and realistic 3D environments from photos or videos, which can enhance the user experience and enable new forms of storytelling.

Entertainment: NeRF can be used to generate 3D models of celebrities, characters, or scenes from movies or games.

NVIDIA Instant NeRF:

But NeRF is also very computationally intensive, and processing complex scenes can take a lot of time. Fortunately, there are new algorithms that can dramatically improve the performance of NeRF, such as NVIDIA Instant NeRF, which can train and render NeRF models in seconds.

It is one of the fastest NeRF techniques to date, achieving more than 1000x speedups in some cases. It also supports VR mode, which allows users to navigate the 3D scene in virtual reality. You can download the software from


Neural Radiance Fields provide an exciting new way to understand and represent the world around us, by capturing the essence and beauty of reality in 3D.

With continued research and development, Neural Radiance Fields will continue to improve, opening up new world of possibilities in Artificial Intelligence.

NeRF is an amazing example of how AI can transform 2D images into 3D scenes or 3D objects, in the blink of an eye. With NeRF, you can unleash your creativity and turn your photos into immersive experiences!

Originally published at https://www.arshopia.com.




Building interactive AR Experiences for Marketing & Entertainment using AR & AI. More info on: arshopia.ai