Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields
Qianqiu Tan Tao Liu Yinling Xie Shuwan Yu Baohua Zhang
The quality of three-dimensional reconstruction is a key factor affecting the
effectiveness of its application in areas such as virtual reality (VR) and
augmented reality (AR) technologies. Neural Radiance Fields (NeRF) can generate
realistic images from any viewpoint. It simultaneously reconstructs the shape,
lighting, and materials of objects, and without surface defects, which breaks
down the barrier between virtuality and reality. The potential spatial
correspondences displayed by NeRF between reconstructed scenes and real-world
scenes offer a wide range of practical applications possibilities. Despite
significant progress in 3D reconstruction since NeRF were introduced, there
remains considerable room for exploration and experimentation. NeRF-based
models are susceptible to interference issues caused by colored "fog" noise.
Additionally, they frequently encounter instabilities and failures while
attempting to reconstruct unbounded scenes. Moreover, the model takes a
significant amount of time to converge, making it even more challenging to use
in such scenarios. Our approach, coined Enhance-NeRF, which adopts joint color
to balance low and high reflectivity objects display, utilizes a decoding
architecture with prior knowledge to improve recognition, and employs
multi-layer performance evaluation mechanisms to enhance learning capacity. It
achieves reconstruction of outdoor scenes within one hour under single-card
condition. Based on experimental results, Enhance-NeRF partially enhances
fitness capability and provides some support to outdoor scene reconstruction.
The Enhance-NeRF method can be used as a plug-and-play component, making it
easy to integrate with other NeRF-based models. The code is available at:
https://github.com/TANQIanQ/Enhance-NeRF