What is Neural Radiance Field (NeRF)?

Machine Learning

What is Neural Radiance Field (NeRF)?

Neural radiance field (NeRF) is a technique that uses advanced machine learning to generate a 3D representation of an object or scene from 2D images. In this technique, an object or an entire scene is encoded into an artificial neural network to predict light intensity. shine — Generate novel 3D views from different angles at any point in the 2D image.

The process is similar to how holograms encode different viewpoints, unlocked by shining a laser at them from different directions. With NeRF, instead of applying light, the app sends a query indicating the desired viewing position and viewport size, and a neural network generates the color and density of each pixel in the resulting image.

NeRF shows amazing potential for representing 3D data more efficiently than other technologies, and may unlock new ways to automatically generate highly realistic 3D objects. Using NeRF in combination with other techniques, it is very likely that his 3D representation of the world can be significantly compressed from several gigabytes to tens of megabytes. time magazine called Silicon Valley chip maker Nvidia’s implementation of NeRF one of the top inventions of 2022. time According to NeRF, “ultimately, digital cameras may be as important to 3D graphics as they are to modern photography.”

Applications of neural radiance fields

NeRF can be used to generate 3D models of objects, render 3D scenes for video games, and be used in virtual and augmented reality environments in the metaverse.

Google has already started using NeRF to transform street map imagery into immersive views in Google Maps. Engineering software company Bentley Systems also uses NeRF as part of its iTwin Capture tool to analyze and generate high-quality 3D representations of objects using cell phone cameras.

Going forward, NeRF may complement other techniques for more efficient, accurate, and realistic representations of 3D objects in metaverses, augmented reality, and digital twins.

One of the big pluses of NeRF is that it works with light fields that directly characterize shapes, textures and material effects. For example, how different materials like cloth and metal look in the light. In contrast, other 3D processing techniques start with geometry and use secondary processes to add texture and material effects.

Early application. Early NeRF was very slow, requiring all photos to be taken in the same lighting conditions and using the same camera. The first generation of his NeRF, described by Google and UC Berkeley researchers in 2020, took him two to three days to train and minutes to generate each view. Early NeRF focused on individual objects such as drum sets, plants, and Lego toys.

continuous innovation. For 2022, Nvidia has developed a variant called Instant NeRF. This variant can capture scene details in about 30 seconds and render different views in about 15 ms. Google researchers also reported new technology for NeRF in the Wild. This is a system that allows her NeRF to be created from photographs taken with different cameras, in different lighting conditions, and using temporary objects in the scene. This paved the way for using NeRF to generate content variations based on simulated lighting conditions and time of day differences.

New NeRF application. Most NeRF applications today render individual objects and scenes from different perspectives rather than combining objects and scenes. For example, the first implementation of Google Maps used NeRF technology to create a short movie simulating a helicopter flying around a building. This removed the challenge of calculating his NeRF and rendering multiple buildings on different devices. However, researchers are exploring ways to extend NeRF to also produce high-quality geospatial data. This makes rendering large scenes easier. NeRF may also eventually offer better ways to store and render other types of images, such as MRIs and ultrasound scans.

How do neural radiation fields work?

the term neural radiance field We will discuss the various elements of the technique.that is neural In the sense that it uses multilayer perceptrons, an old neural network architecture, to represent images. shine It refers to the fact that this neural network models the brightness and color of light rays from different perspectives. field is a mathematical term that describes a model that uses a particular structure to transform various inputs into outputs.

NeRF differs from other deep learning techniques in that it uses a series of images to train a single fully connected neural network. This neural network can only be used to generate new views of that one object. Deep learning, by contrast, starts by using labeled data to train a neural network.

The actual operation of the neural network takes as input the 3D physical position and 2D orientation (left-right and up-down) where the simulated camera is pointing, and produces a response as the color and density of each pixel in the image. This reflects how rays bounce off objects from that view in space.

Training Neural Radiance Fields

NeRF is trained from images of an object or scene captured from different viewpoints. The training algorithm then calculates the relative positions at which each image was taken and uses this data to adjust the weights of the neural network nodes until the output matches those images.

Here are the details of the process:

  1. The training process starts with a collection of images of a single object or scene taken from different perspectives, ideally from the same camera. In the first step, a computational photo algorithm calculates the camera position and orientation for each photo in the photo collection.
  2. Information from photos and locations is used to train a neural network. The difference between these image pixels and the expected result is used to adjust the neural network weights. This process is repeated about 200,000 times until the network converges to a good NeRF. Early versions took days, but as mentioned earlier, his recent Nvidia optimizations allow everything to run in parallel in tens of seconds.
  3. There is one more step that the NeRF developers are still trying to figure out. When the researcher first started experimenting with his NeRF, the images looked like smooth, blurry blobs, lacking the rich textures of natural objects. So I added a bit of digital noise to the rays to enhance NeRF’s ability to capture finer textures. This early noise consisted of relatively simple cosine and sine waves, but later versions turned to Fourier transforms for better results. Adjusting this noise level will help adjust to the desired resolution. Too little and the scene will look smooth and washed out. Too much and it looks pixelated. While most researchers have stuck with the Fourier transform, Nvidia has taken it a step further with a new encoding technique called multi-resolution hash encoding, which Nvidia cites as a key factor in producing good results. I was.

What are the limitations and challenges of neural radiation fields?

Early NeRF required a large amount of computational power, a large number of images, and was not easy to train. Computing and training is less of an issue now, but we still need a lot of images. Other key NeRF challenges include speed, editability, and configurability.

  • It takes time, but not much. In terms of speed, training NeRF requires hundreds of thousands of training iterations. Early versions took days on a single GPU. However, Nvidia has demonstrated how to overcome this challenge through more efficient parallelization and optimization that can generate a new NeRF in tens of seconds and render a new view in tens of milliseconds.
  • It’s hard to edit, but it’s getting easier. The editability challenge is a little tricky. NeRF captures different views of an object and collects them into a neural network. This is much less intuitive to edit than other kinds of 3D formats, such as 3D meshes representing the surface of an object, or voxels (3D pixels) representing her 3D structure of an object. Google’s work on NeRF in the Wild suggested ways to change colors, lighting, and remove unwanted objects that appear in some images. For example, you can remove buses and tourists from a photo of Berlin’s Brandenburg Gate taken by multiple people.
  • Composability remains a hurdle. The composability challenge is related to the fact that researchers have not found an easy way to combine multiple NeRFs to compose a larger scene. This can be difficult for certain use cases, such as rendering a simulated factory layout consisting of her NeRFs for individual pieces of equipment, or creating a virtual world combining his NeRFs for different buildings. there is.

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