Recursive-NeRF: An Efficient and Dynamically Growing NeRF.

Area of Science:

• Computer Vision
• Computer Graphics
• Machine Learning

Background:

• Neural Radiance Fields (NeRF) offer high-quality view synthesis but suffer from significant computational demands.
• Existing NeRF methods require substantial time to render high-resolution images, limiting practical applications.

Purpose of the Study:

• To develop an efficient and adaptive rendering and training approach for NeRF.
• To balance rendering quality and computational efficiency in view synthesis.

Main Methods:

• Introduced Recursive-NeRF, a method that employs a level-of-detail strategy.
• Learns uncertainties for query coordinates to adaptively allocate computational resources.
• Utilizes progressively larger neural networks for regions with higher uncertainty.

Main Results:

• Recursive-NeRF demonstrates improved efficiency compared to standard NeRF.
• Achieves state-of-the-art rendering quality.
• Adaptive computation leads to faster rendering times without compromising visual fidelity.

Conclusions:

• Recursive-NeRF offers a practical solution for efficient, high-quality view synthesis.
• The adaptive, level-of-detail approach effectively manages computational complexity.
• This method enhances the scalability and usability of implicit neural representations.