Neural rendering is an emerging technique in deep learning that aims to augment the classical pipeline of computer graphics with neural networks.
A neural rendering algorithm will require a set of images representing different angles of the same scene. These images will then be fed into a neural network to create a model that can output new angles of the same scene.
The brilliance behind neural rendering lies in how it can accurately recreate detailed photorealistic scenes without having to rely on classical methods that may be more computationally demanding.
Before diving into how neural rendering works, let’s go over the basics of classical rendering.
What is Classical Rendering?
Let’s first understand the typical methods used in classical rendering.
Classical rendering refers to the set of techniques used to create a 2D image of a three-dimensional scene. Also known as image synthesis, classical rendering uses various algorithms to simulate how light interacts with different types of objects.
For example, rendering a solid brick will require a particular set of algorithms to determine the position of the shadow or how well-lit either side of the wall will be. Similarly, objects that reflect or refract light, such as a mirror, a shiny object, or a body of water, will require their own techniques as well.
In classical rendering, each asset is represented with a polygon mesh. A shader program will then use the polygon as input to determine how the object will look like given the specified lighting and angle.
Realistic rendering will require much more computation power since our assets end up having millions of polygons to use as input. The computer-generated output that’s common in Hollywood blockbusters typically takes weeks or even months to render and can cost millions of dollars.
The ray tracing approach is particularly costly because each pixel in the final image requires a calculation of the path light takes from the light source to the object and to the camera.
Advancements in hardware have made graphics rendering much more accessible for users. For example, many of the latest video games allow ray-traced effects such as photo-realistic reflections and shadows as long as their hardware is up to the task.
The latest GPUs (graphic processing units) are built specifically to help the CPU handle the highly complex calculations required to render photo-realistic graphics.
The Rise of Neural Rendering
Neural rendering tries to tackle the rendering problem in a different way. Instead of using algorithms to simulate how light interacts with objects, what if we created a model that learns how a scene should look from a certain angle?
You can think of it as a shortcut to creating photorealistic scenes. With neural rendering, we don’t need to calculate how light interacts with an object, we just need enough training data.
This approach allows researchers to create high-quality renders of complex scenes without having to perform
What are Neural fields?
As mentioned earlier, most 3D renders use polygon meshes to store data on the shape and texture of each object.
However, neural fields are gaining popularity as an alternative method of representing three-dimensional objects. Unlike polygon meshes, neural fields are differentiable and continuous.
What do we mean when we say neural fields are differentiable?
A 2D output from a neural field can now be trained to become photorealistic by simply adjusting the weights of the neural network.
Using neural fields, we no longer need to simulate the physics of light to render a scene. The knowledge of how the final render will be lit is now stored implicitly inside the weights of our neural network.
This allows us to create novel images and videos relatively quickly from just a handful of photos or video footage.
How to Train a Neural Field?
Now that we know the basics of how a neural field works, let’s take a look at how researchers are able to train a neural radiance field or NeRF.
First, we’ll need to sample the random coordinates of a scene and feed them into a neural network. This network will then be able to produce field quantities.
The produced field quantities are considered samples from the desired reconstruction domain of the scene we want to create.
We’ll then need to map the reconstruction to actual 2D images. An algorithm will then calculate the reconstruction error. This error will guide the neural network to optimize its ability to reconstruct the scene.
Applications of Neural Rendering
Novel View Synthesis
Novel view synthesis refers to the task of creating camera perspectives from new angles using data from a limited number of perspectives.
Neural rendering techniques try to guess the relative position of the camera for each image in the dataset and feed that data into a neural network.
The neural network will then create a 3D representation of the scene where each point in the 3D space has an associated color and density.
A new implementation of NeRFs in Google Street View uses novel view synthesis to allow users to explore real-world locations as if they were controlling a camera taking a video. This allows tourists to explore destinations in an immersive way before deciding to travel to a specific site.
Advanced techniques in neural rendering can also pave the way for more realistic digital avatars. These avatars can then be used for various roles such as virtual assistants or customer service, or as a way for users to insert their likeness in a video game or simulated render.
For example, a paper published in March 2023 suggests using neural rendering techniques to create a photo-realistic avatar after a few minutes of video footage.
Neural rendering is an exciting field of study that has the potential to change the entire computer graphics industry.
The technology could lower the barrier of entry for the creation of 3D assets. Visual effects teams may no longer have to wait days to render a few minutes of photo-realistic graphics.
Combining the technology with existing VR and AR applications may also allow developers to create more immersive experiences.
What do you think is the true potential for neural rendering?