NeRF Studio Made Easy - From Computer Vision Scientists' rooms to Software Engineers' desks.

NeRF Studio came into being to make the development and adoption of all the NeRF papers and researches into a `plug n play` modular framework which makes it creation, training and visualisation easy. Not only this, they have soul soothing documentation which makes the learning in matter of hours. Then other question will be why I am poking my nose when everything is sorted and functional. The answer to this is, it is still uses computer vision scientists' jargons in places and none of the design diagram explain in terms of a software engineer or a software architect.

NeRF

NeRF (Neural Radiance Fields) is a 3D scene representation model that uses deep neural networks to estimate the radiance (intensity and color) of a scene at any given 3D point. The model takes as input a set of 2D images of a scene captured from different viewpoints, and their corresponding camera parameters. It then learns a function that maps the 3D spatial location and viewing direction to the radiance value, allowing for high-quality rendering of the scene from novel viewpoints.

Mathematically, NeRF is defined as a function f that maps a 3D point p and a viewing direction vector d to a color and opacity value:

color, sigma = f(p, d)

where color represents the RGB color of the scene at point p, sigma represents the opacity (how much light is absorbed by the scene at that point), and p and d are 3D vectors representing the spatial location and viewing direction, respectively.

The function f is learned by training a deep neural network on a set of images and corresponding camera parameters, using a technique called volume rendering. The network takes as input the 3D point p and the viewing direction d, and produces the color and opacity values as output.

Note: Original NeRF paper build upon the previous efforts of regressing a Signed Distance Function (SDF) for depth and normals to regressing density and color. Later paper brought multiple encoding technique to make the view synthesis super fast. Sorry I forgot, no CV scientist jargons

NeRF Studio - One Pager Landscape

We will not go through each of these in details but we will define so that we know what is happening when NeRF is trained or you want to implement your own NeRF.

Ray Samplers

One of the key components of NeRF is the use of "ray sampling" to estimate the appearance and geometry of the scene. Here are some of the ray sampling techniques used in NeRF:

1. Stratified Sampling: This technique involves dividing the ray into a fixed number of evenly spaced samples, which are then jittered randomly within each segment to produce a more uniform distribution. Stratified sampling helps to ensure that the entire ray is sampled evenly and can help to reduce the amount of noise in the reconstructed image.
2. Hierarchical Sampling: This technique involves dividing the ray into a series of sub-rays, each with decreasing step sizes. The radiance field is then estimated at each sub-ray using a coarse-to-fine approach. Hierarchical sampling is useful for reconstructing fine details in the scene and can help to reduce aliasing artifacts.
3. Spaced Sampling: This technique involves sampling the ray at irregular intervals, with more samples in regions where the radiance field changes rapidly and fewer samples in regions where it is smoother. Spaced sampling can help to improve the accuracy of the reconstruction while minimizing the number of samples required.
4. Probability Density Function (PDF) Sampling: This technique involves sampling the ray based on a probability distribution that is proportional to the radiance field. PDF sampling helps to ensure that the most important regions of the scene are sampled more densely, while reducing the number of samples required in regions where the radiance field is less significant.

Overall, the choice of ray sampling technique depends on the specific requirements of the reconstruction task and the properties of the scene being reconstructed. By combining these techniques, NeRF is able to accurately estimate the appearance and geometry of complex 3D scenes from 2D images.

package: nerfstudio.model_components.ray_sampler.*        

NeRF Models

Here are some of the variants of NeRF that can be used in NeRFStudio:

1. Nerfacto: Nerfacto is a variant of NeRF that incorporates additional physical constraints, such as energy conservation and reciprocity, into the radiance field. These constraints can help to improve the accuracy of the reconstruction by ensuring that the estimated radiance field is physically plausible.
2. Instant-NGP: Instant-NGP is a variant of NeRF that uses a pre-trained deep neural network to estimate the geometry and radiance field of the scene. This approach can help to reduce the computational resources required for reconstruction and can be used to generate real-time 3D visualizations.
3. Vanilla NeRF: Vanilla NeRF is the basic version of the NeRF algorithm, which uses a neural network to model the radiance field of the scene. This approach can be computationally expensive, especially for complex scenes, but can produce high-quality reconstructions with accurate geometry and appearance.
4. Min-NeRF: Min-NeRF is a variant of NeRF that uses a minimal number of parameters to estimate the radiance field. This approach can help to reduce the computational resources required for reconstruction while maintaining high levels of accuracy.

Overall, the choice of NeRF variant depends on the specific requirements of the reconstruction task and the properties of the scene being reconstructed. By using NeRFStudio, researchers and developers can easily experiment with different NeRF variants and visualize the results in real-time.

$ ns-train -h #Use this command to list all available NeRF models to train         

Encoders

In NeRFStudio, several encoder techniques are used to process the input data and generate features that are then used to estimate the radiance field. Here's an overview of each of the encoder techniques used in NeRFStudio and how they are used in different types of NeRF models:

1. Hierarchical Encoder: The Hierarchical Encoder is a type of encoder that uses a series of convolutional neural networks (CNNs) with decreasing resolutions to process the input data. The output of each CNN is then concatenated with the input data and passed to the next CNN in the sequence. This approach helps to capture both local and global features of the scene and can improve the accuracy of the reconstruction. Hierarchical encoding is used in several NeRF variants, including NeRF, NeRF++ and NeRFactor.
2. Hash Encoder: The Hash Encoder is a type of encoder that uses a hash function to map the input data to a fixed-length feature vector. This approach can be used to reduce the dimensionality of the input data and improve the efficiency of the reconstruction. Hash encoding is used in NeRFactor, which incorporates additional physical constraints into the radiance field to improve the accuracy of the reconstruction.
3. Harmonic Encoder: The Harmonic Encoder is a type of encoder that uses harmonic functions to model the geometry of the scene. This approach can be used to represent complex geometry (such as non-rigid objects) and can help to improve the accuracy of the reconstruction. Harmonic encoding is used in NeRFactor to incorporate additional physical constraints into the radiance field.
4. Random Fourier Features (RFF) Encoder: The RFF Encoder is a type of encoder that uses random Fourier features to approximate the radiance field. This approach can be used to reduce the computational resources required for the reconstruction while maintaining high levels of accuracy. RFF encoding is used in NeRF-VAE, which incorporates a Variational Autoencoder (VAE) to learn a low-dimensional latent representation of the scene.

Overall, the choice of encoder technique depends on the specific requirements of the reconstruction task and the properties of the scene being reconstructed. By using NeRFStudio, researchers and developers can experiment with different encoder techniques and evaluate their effectiveness in real-time.

package: nerfstudio.field_components.encoding.* # check this to see all implemented encoders        

Renderers

In NeRFStudio, renderers are used to generate synthetic images from the estimated radiance field of a scene. Here are some of the renderer techniques used in NeRFStudio:

1. RGB Renderer: The RGB Renderer is a type of renderer that generates synthetic images by computing the RGB color values at each pixel based on the estimated radiance field. This approach is used when generating photorealistic images that accurately capture the color information of the scene.
2. Accumulation Renderer: The Accumulation Renderer is a type of renderer that generates synthetic images by accumulating the contributions of multiple samples along each ray. This approach can be used to reduce noise in the reconstructed images and improve the overall image quality.
3. Depth Renderer: The Depth Renderer is a type of renderer that generates synthetic depth maps based on the estimated radiance field. This approach can be used to extract depth information from the scene and enable 3D reconstruction.
4. Normal Renderer: The Normal Renderer is a type of renderer that generates synthetic surface normal maps based on the estimated radiance field. This approach can be used to extract surface orientation information from the scene and enable various computer vision tasks such as object detection and segmentation.

Overall, the choice of renderer technique depends on the specific requirements of the application and the properties of the scene being reconstructed. By using NeRFStudio, researchers and developers can experiment with different renderer techniques and evaluate their effectiveness in real-time. Additionally, NeRFStudio allows for the combination of multiple renderer techniques to generate synthetic images with a variety of features and properties.

package: nerfstudio.model_components.renderers.* # Check to see all implemented Renderers in NeRFStudio        

Camera Models

Camera models are used to simulate the projection of the 3D scene onto a 2D image plane. Here are some of the camera models used in NeRFStudio:

1. Perspective Camera: The Perspective Camera is a type of camera model that simulates the behavior of a pinhole camera. In this model, rays are projected from a point in the scene (the camera center) through the image plane onto the scene. This model is commonly used in computer graphics and computer vision applications and is well-suited for simulating the behavior of physical cameras.
2. Fisheye Camera: The Fisheye Camera is a type of camera model that simulates the behavior of a fisheye lens. In this model, the lens projects rays from the scene onto the image plane in a non-linear manner, resulting in a distorted image. This model is commonly used in applications that require a wide field of view, such as panoramic photography.
3. Equirectangular Camera: The Equirectangular Camera is a type of camera model that simulates the behavior of a spherical camera. In this model, the scene is projected onto a 2D plane in a way that preserves the angular distance between points on the sphere. This model is commonly used in applications that require spherical images, such as virtual reality and 360-degree video.
4. Orthographic Camera: The Orthographic Camera is a type of camera model that simulates the behavior of a pinhole camera with an infinite focal length. In this model, rays are projected from the scene onto the image plane in parallel, resulting in a distorted image. This model is commonly used in applications that require an isometric or top-down view of the scene.

package: nerfstudio.cameras.cameras.* # check to see all camera objects        

Client Enablers

NeRF Viewer

Role of WebSocket and WebRTC

1. WebSocket: WebSocket is a protocol that provides a bi-directional, full-duplex communication channel between a client and a server over a single TCP connection. In NeRF Studio, WebSocket is used to establish a real-time connection between the client (i.e., the user's web browser) and the server (i.e., the NeRF rendering engine running on a remote machine). This connection allows the user to interact with the scene and see the rendered images in real-time, without having to download the entire scene or refresh the web page.
2. WebRTC: WebRTC is a technology that enables real-time communication between web browsers. In NeRF Studio, WebRTC is used to stream the rendered images from the remote rendering engine to the user's web browser. This allows the user to see the rendered images in real-time and interact with the scene as it is being rendered. WebRTC also allows for secure and encrypted communication, which is important for protecting sensitive data such as user inputs and rendered images.

What is Bridge Server and Why there is messaging Queue ?

1. Bridge Server: The Bridge Server is a component of the NeRF Studio architecture that acts as a communication bridge between the client (i.e., the user's web browser) and the server (i.e., the NeRF rendering engine running on a remote machine). The Bridge Server receives messages from the client and forwards them to the server, and vice versa. This allows the user to interact with the scene and see the rendered images in real-time, without having to download the entire scene or refresh the web page. The Bridge Server also provides load balancing and fault tolerance capabilities, ensuring that the communication channel remains stable even under high traffic or server failure conditions.
2. Message Queue: The Message Queue is a component of the NeRF Studio architecture that is used to manage the communication between the Bridge Server and the server running the NeRF rendering engine. The Message Queue stores messages sent by the client and forwards them to the server for processing. This allows the server to process messages in a timely and efficient manner, without overloading the communication channel. The Message Queue also provides fault tolerance and message persistence capabilities, ensuring that messages are not lost in case of a server failure or network outage.

What's current?

NeRFStudio has added it's plugin for Blender which has taken VFX industry by surprise. Also, it has given the path for Unreal Engine today.

Conclusion

As the guy from Two Minutes Paper says, `What a time to be alive?!! :-)`. But never understood the person who do that is the real guy or the AI ?