Neural Radiance Field (NeRF) is a deep learning technique that uses sparse two-dimensional photographs to reconstruct a three-dimensional representation of a scene.

If you have sparse two-dimensional photographs of a scene and want to recreate it in three dimensions, you can use a deep learning technique called a neural radiance field (NeRF). The NeRF model facilitates the acquisition of knowledge on generating new views, the scene's structure, and the scene's reflective characteristics.

Collaborative learning also allows for acquiring additional scene attributes, like camera postures. With NeRF, new perspectives can be rendered with lifelike accuracy. Since its 2020 debut, it has garnered considerable interest due to its possible uses in computer graphics and content production.

A NeRF must be retrained for each situation. The first stage is to collect photographs of the scene from various angles and camera poses. These are normal 2D photos that don't require any special camera or software. Any camera can generate datasets if the settings and capture method fulfils the requirements for SfM.

It necessitates tracking the camera's position and orientation, frequently accomplished using a combination of SLAM, GPS, and inertial estimation. Researchers often use synthetic data to assess NeRF and related approaches. Photos (generated using classic non-learned methods) and camera postures are repeatable and error-free for such data.

Training

Camera rays are marched around the scene for each sparse perspective (picture and camera pose), yielding a set of 3D points with a specific radiance direction (into the camera). The MLP predicts volume density and emitted radiance at these sites. The image is then created using conventional volume rendering. Because this method is fully differentiable, the error between the predicted and original images can be reduced via gradient descent across several views, encouraging the MLP to create a cohesive scene model.

Improvements

Initial iterations of NeRF exhibited suboptimal optimization speed and necessitated capturing all input views using a consistent camera and lighting setup. These items demonstrated optimal performance when confined to orbiting around discrete entities, such as a drum set, plants, or small toys. Since its initial publication in 2020, the NeRF algorithm has undergone numerous enhancements, including adaptations tailored for specific applications.

Applications

NeRFs have numerous applications and are becoming increasingly popular as they are integrated into user-friendly apps.

Content creation

NeRFs offer enormous promise in content development, as on-demand photorealistic views are beneficial. The technique democratizes a space that was previously only accessible to large teams of VFX artists and pricey assets. Neural radiance fields enable anyone with a camera to construct visually appealing 3D worlds. NeRF has been integrated with generative AI, allowing people without modelling skills to adjust to photorealistic 3D settings. NeRFs have potential applications in video production, computer graphics, and product design.

Interactive content

NeRFs' photorealism makes them ideal for immersion-based applications such as virtual reality and video games. They can be used alongside traditional rendering techniques to insert synthetic objects and generate realistic virtual experiences.

Medical imaging

NeRFs have been utilized to reconstruct 3D CT scans from sparse or single X-ray images. The model generated high-fidelity visualizations of chest and knee data. This procedure can protect patients from excessive ionizing radiation, providing a safer diagnosis.

Robotics and Autonomy

NeRFs' unique ability to understand translucent and reflecting objects makes them valuable for robots interacting in such settings. NeRF enabled a robot arm to precisely manage a transparent wine glass, which would have been difficult for typical computer vision.

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