核心概念
Developing a fast method to update Neural Radiance Fields (NeRFs) for object reconfigurations using sparse view guidance.
摘要
The article introduces a novel approach to updating NeRFs for object reconfigurations, addressing the challenge of accommodating changes in static scenes. By utilizing sparse new images and a helper NeRF model, the method can quickly update pre-trained NeRFs in just 1 to 2 minutes. The core idea involves identifying scene changes and updating the NeRF accordingly, sidestepping optimization difficulties. This method imposes no constraints on NeRF pre-training and requires no extra user input or explicit semantic priors. It is significantly faster than retraining NeRF from scratch while maintaining performance.
統計資料
Our method updates pre-trained NeRFs in around 1 to 2 minutes.
The approach sidesteps optimization difficulties by using a helper NeRF model.
The method imposes no constraints on NeRF pre-training.
It requires no extra user input or explicit semantic priors.
The update training is about 20 to 60 times faster than NeRF re-training.
引述
"Our method takes only sparse new images of the altered scene as extra inputs and updates the pre-trained NeRF in around 1 to 2 minutes."
"Our core idea involves using a second helper NeRF model to learn local geometry and appearance changes, which accelerates the process."
"Our method imposes no constraints on NeRF pre-training and requires no additional user input, maximizing convenience."