Neural Radiance Fields: Learning The Continuous 3D Representation Of A Volumetric Scene With A Neural Network:
A recent development in computer graphics called Neural Radiance Fields (NeRF) makes it possible to create stunning 3D renderings of intricate and dynamic situations. NeRF is a deep learning-based methodology that creates an ongoing 3D representation of a scene from a collection of 2D photos taken from various angles.
NeRF’s central idea is to map each 3D point in space to its associated radiance value using a function to represent the 3D geometry and look of a scene (i.e., the intensity and color of the light passing through that point). A deep neural network is used to simulate this function, which takes as inputs the 3D coordinates of a point and the direction in which a virtual camera is looking, and returns the radiance value that corresponds to those coordinates.
A collection of 2D photos and the matching camera postures are utilized as input to train the NeRF model. The images are first transformed into a collection of viewing rays, which stand in for the path taken by light rays as they traverse a scene on their way to the camera. Using the associated 3D coordinates and viewing direction, the NeRF model is then trained to predict the radiance value for every location along each viewing ray.
By casting rays from the virtual camera and utilizing the NeRF function to calculate the associated radiance values, the NeRF model may be used to generate lifelike 3D scenes from any viewpoint once it has been trained. This makes it feasible to produce incredibly accurate and lifelike 3D renderings of scenes that would otherwise be difficult or impossible to do so using conventional computer graphics methods.
Many possible uses for NeRF exist, including gaming, virtual and augmented reality, movie, and video production, and scientific simulations. Unfortunately, it is currently constrained by the quantity of computing work and training data needed. NeRF, however, offers a substantial advancement in the creation of dynamic and realistic 3D visualizations.
The computer vision community has been quite interested in NeRF since Mildenhall et al. first mentioned it in a research article in 2020. NeRF’s foundation is the idea that a 3D scene should be represented as a continuous function rather than a discrete object like a mesh or point cloud. As a result, NeRF can represent scenes with extremely high spatial resolution without experiencing the drawbacks of discretization, such as holes and inconsistent representations.
NeRF uses a fully-connected neural network that receives a point’s 3D position as input and outputs the radiance value associated with that position. The network is intended to be permutation-invariant, which means that the output is independent of the order in which the points are presented to the network. This is significant because, in a continuous representation, the order of the points is meaningless.
A random 3D point in the scene is used as the network’s input during training, and its associated radiance value is produced as the output. The gap between the anticipated radiance value and the actual radiance value shown in the scene photos is known as the rendering loss, and the network is trained to reduce this discrepancy. By rendering the scene using the expected radiance values and comparing the generated image to the observed image, the rendering loss is calculated.
NeRF samples a series of rays from the camera location and uses the NeRF function to forecast the radiance values of the 3D points along the rays in order to generate an image of the scene from a specific vantage point. The final image is created by integrating the radiance values along the rays. NeRF can create stunning photographs with intricate lighting effects like reflections and shadows, and it may be utilized to create fresh perspectives on the environment that weren’t seen in the training set.
NeRF is difficult to employ for real-time applications due to a number of drawbacks, such as its high computational cost and memory needs. NeRF also cannot simulate dynamic objects or situations with moving cameras; it can only model static scenes.
NeRF, however, marks a significant achievement in the area of 3D computer vision and has the potential to be used in a variety of contexts, such as virtual reality, augmented reality, and video game production.
The research community has suggested a number of extensions and modifications to work around NeRF’s drawbacks. One of these is NeRF++, which increases the computing effectiveness of NeRF by using a hierarchical network topology.
The network can concentrate on modeling the scene’s tiny details just where it is necessary thanks to the hierarchical structure, while the hierarchy’s higher layers model the scene’s coarser aspects. As a result, both memory and computational efficiency significantly improve.
Dynamic Neural Radiance Fields (D-NeRF) is an extension of NeRF that can model dynamic objects and scenes with moving cameras. In order to produce continuous representations of the scene across time, D-NeRF accomplishes this by learning a different neural network for each frame of the sequence.
This preserves NeRF’s high-quality rendering capabilities while enabling D-NeRF to handle dynamic scenes and objects. Neural Scene Graphs and Occupancy Networks are two further NeRF variants that model the scene using graph-based representations and implicit functions, respectively.
NeRF and its extensions have already been used in a number of industries, including virtual tours, augmented reality, and movie production. NeRF, for instance, has been used to create 3D models of historic sites that users may tour in a virtual setting. It has also been applied to the creation of photorealistic backgrounds and visual effects for films.
In terms of 3D computer vision, NeRF represents a substantial advancement and has the potential to be used in a variety of future applications. NeRF and its extensions’ computational expense needs to be reduced, nevertheless, in order to make it more practical for real-time applications.