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

翻译：三维重建质量是影响其在虚拟现实（VR）和增强现实（AR）技术等领域应用效果的关键因素。神经辐射场（NeRF）可从任意视角生成逼真图像，同时重建物体的形状、光照与材质，且不产生表面缺陷，打破了虚拟与现实的壁垒。NeRF在重建场景与现实场景之间展示的潜在空间对应关系，为其提供了广泛的实用可能性。尽管自NeRF问世以来三维重建取得了显著进展，但仍有大量探索与实验空间。基于NeRF的模型易受彩色“雾状”噪声的干扰问题影响；此外，在尝试重建无界场景时，常出现不稳定和失败的情况；同时，模型收敛耗时较长，使得在此类场景中的应用更加困难。我们提出的方法Enhance-NeRF采用联合色彩平衡高低反射率物体的显示，利用带先验知识的解码架构提升识别能力，并引入多层性能评估机制增强学习容量，可在单卡条件下于一小时内完成室外场景重建。实验结果表明，Enhance-NeRF部分提升了拟合能力，并为室外场景重建提供了支持。Enhance-NeRF可作为即插即用组件，便于与其他基于NeRF的模型集成。代码公开于：https://github.com/TANQIanQ/Enhance-NeRF