NeRF (Neural Radiance fields) is a deep learning technique for learning representations of 3D scenes from a set of 2D images and being able to render new perspectives. This technique has attracted widespread attention in the fields of computer vision and graphics because it is able to generate high-quality renderings of 3D scenes. NeRF technology has shown great potential in the fields of computer vision and graphics with its unique implicit representation and powerful new perspective synthesis capabilities. By deeply understanding its basic principles and workflows, we can better apply this technology to solve practical problems. With the continuous deepening of research, NeRF technology has shown broad application prospects in many fields, from virtual reality to robot navigation, NeRF is constantly pushing the boundaries of technology.
What is NeRF
NeRF (Neural Radiance Fields) is a computer vision technology for generating high-quality 3D reconstruction models. Through deep learning technology, the geometry and texture information of objects are extracted from images of multiple perspectives, and then a continuous 3D radiation field is generated using this information. It can present highly realistic 3D models at any angle and distance. NeRF technology has broad application prospects in computer graphics, virtual reality, augmented reality and other fields.
How NeRF works？
NeRF represents a 3D scene as a continuous 5D radiation field, which contains the position (x, yz) and viewing direction (0, φ) information of each point in the scene. The neural network F0 accepts these inputs and outputs the self-luminous color c and voxel density σ of the point. NeRF uses volume rendering technology to obtain the final pixel color by integrating the voxel density and color information on the light. This process can be expressed as: C(r)= ∫tntfT(t)σ(r(t)c(r(t),d)dtC(r)= ∫tntfT(t)σ(r(t)c(r(t),d)dt Where C(r)C(r) is the color on the light r, T(t)T(t) is the cumulative transmittance of the light from tntn to t, indicating the probability that the light did not hit any particle before t.
Input multi-view images (including pixel coordinates, pixel colors) and camera internal parameters, pose and other data. Use the ray marching method to generate rays, and obtain the coordinates of the spatial sampling points through random sampling and importance sampling. The NeRF network outputs the volume density σ and color c of the sampling points. According to the principle of volume rendering, the volume density and color information of the sampling points are integrated to obtain the final pixel color. The generated image is compared with the real image for loss calculation to train the neural network.
Main applications of NeRF
New perspective synthesis: NeRF technology can be used to generate high-quality 3D scene images from never-before-seen angles, which is very valuable for virtual reality (VR) and augmented reality (AR) applications.
3D reconstruction: In filmmaking and game development, NeRF can be used to reconstruct detailed 3D models from existing 2D images.
Virtual photography: NeRF can be used to create realistic virtual photography scenes that allow users to view and interact from any angle.
Robotic navigation: In the field of robotics, NeRF can be used to improve the robot's environmental perception ability and assist navigation and decision-making by generating new perspectives.
Challenges facing NeRF
NeRF (Neural Radiance Although NeRF technology has made significant progress in 3D scene reconstruction and new perspective synthesis, it still faces a series of challenges in practical applications:
Different imaging principles: NeRF technology was originally designed to process RGB-based images, which capture the external features of objects through visible light. However, in the field of medical imaging, such as X-ray imaging, the imaging principle is fundamentally different from visible light imaging. X-ray imaging penetrates objects and attenuates, mainly revealing internal structures. NeRF needs to adapt to the unique characteristics of X-ray imaging, and significant adjustments to the algorithm may be required.
Detailed internal structure required: Medical images require highly detailed internal structures to accurately represent the complex internal structure of the body. NeRF must be able to transform from 2D images to Capturing these details in the image and accurately rendering complex internal anatomical structures, such as subtle distinctions between different types of tissue or subtle abnormalities in early disease, can be challenging.
Ambiguity of object boundaries: Medical images often encounter the problem of fuzzy object boundaries between different types of tissue or between pathological and healthy tissue. This ambiguity in boundary definition poses a significant challenge to NeRF because these models rely on clear distinctions in visual data to accurately reconstruct 3D space.
The importance of color density: In medical imaging, changes in color density can convey key information, such as the presence of blood flow, changes in tissue density, or the accumulation of contrast agents. NeRF must adapt to and accurately reproduce these color density changes to convey the correct information in the synthesized 3D image.
Computational resources and scalability: NeRF models require a lot of computing resources when processing large-scale scenes or high-resolution images. This limits the application of NeRF in resource-constrained environments and may result in long training and rendering times.
Prospects for the development of NeRF
As a cutting-edge 3D vision technology, NeRF (Neural Radiance Fields) technology has broad development prospects. It can reconstruct high-quality 3D scenes from sparse 2D images and realize image synthesis from new perspectives. With the advancement of technology, NeRF is moving towards the direction of achieving real-time rendering and interaction, which is particularly important for fields such as virtual reality and augmented reality. NeRF technology has shown great application potential in many fields such as film production, game development, architectural design, and cultural relics protection. Researchers are exploring ways to optimize NeRF to improve its computational efficiency and scalability so that it can handle larger-scale scenes. Although NeRF technology faces challenges in terms of training data requirements and computing resource consumption, the continuous development of technology is expected to solve these problems and further expand the scope of NeRF's application.
