Core Concepts
Neural Radiation Field (NeRF) technology can learn a 3D implicit model of a scene from 2D images and synthesize realistic novel view images, but the rendering quality needs to be improved. Researchers have proposed various methods to address this issue.
Abstract
The article discusses the challenges in improving the rendering quality of Neural Radiation Field (NeRF) technology for novel view synthesis and 3D reconstruction. The key factors that affect the rendering quality include insufficient training data, low image resolution, and high scene complexity.
The article reviews the latest techniques from four aspects to enhance the rendering quality of NeRF:
Prior Guidance: Introducing prior knowledge, such as visibility priors, depth priors, and scene priors, to assist NeRF in handling sparse input views, reflective/refractive objects, and complex scenes.
Adjusting NeRF Model Structure: Modifying the basic MLP model structure of NeRF to better encode high-frequency information, separate appearance and lighting, and represent local radiation fields for efficient structure-aware 3D scene representation.
Adopting Pre-trained Models: Leveraging pre-trained diffusion models and autoencoders to optimize the NeRF representation and achieve improved rendering quality.
High-Resolution Image Processing: Combining NeRF with super-resolution techniques and aligning-aware strategies to address the limitations of NeRF in handling high-resolution input images.
The article also discusses the future research directions, including applying NeRF to large-scale and unbounded scenes, accelerating rendering speed, and further improving rendering quality without compromising efficiency.
Stats
"NeRF provides an accurate and simple method to represent 3D scenes using an implicit function based on MLP."
"Training NeRF on sparse input views can lead to overfitting and incorrect scene depth estimation, resulting in artifacts in the rendered new views."
"NeRF can cause blurry or distorted renderings when reflective and refractive objects are present."
"When the input image scene becomes complex, the basic MLP structure of NeRF is not enough to render high-quality new perspective images."
"NeRF's inherent multi-layer perceptron has difficulty learning high-frequency details, leading to blur problems in high-resolution rendering."
Quotes
"NeRF represents a three-dimensional scene as a radiation field approximated by a neural network."
"The radiance field describes the volumetric density and color of each point in the scene for every viewing direction, and represents the static scene as a continuous five-dimensional vector function."