With the growing popularity of neural rendering, there has been an increasing number of neural implicit multi-view reconstruction methods. While many models have been enhanced in terms of positional encoding, sampling, rendering, and other aspects to improve the reconstruction quality, current methods do not fully leverage the information among neighboring pixels during the reconstruction process. To address this issue, we propose an enhanced model called BundleRecon. In the existing approaches, sampling is performed by a single ray that corresponds to a single pixel. In contrast, our model samples a patch of pixels using a bundle of rays, which incorporates information from neighboring pixels. Furthermore, we design bundle-based constraints to further improve the reconstruction quality. Experimental results demonstrate that BundleRecon is compatible with the existing neural implicit multi-view reconstruction methods and can improve their reconstruction quality.

翻译：随着神经渲染技术的日益普及，神经隐式多视图重建方法也不断涌现。尽管许多模型在位置编码、采样、渲染等方面进行了改进以提升重建质量，但现有方法在重建过程中并未充分利用相邻像素间的信息。为解决此问题，我们提出了一种增强模型，称为BundleRecon。在现有方法中，采样是通过与单个像素对应的单条射线进行的。相比之下，我们的模型使用一束射线来采样一个像素块，从而融入了相邻像素的信息。此外，我们设计了基于束的约束以进一步改善重建质量。实验结果表明，BundleRecon与现有神经隐式多视图重建方法兼容，并能提升其重建质量。