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.

本研究提出了一种增强的神经隐式多视图重建模型 ——BundleRecon，该模型通过使用捆绑光线束的方式采样一组像素，从而充分利用邻近像素之间的信息，并设计基于光线束的约束以进一步提高重建质量。实验证明，该模型与现有的神经隐式多视图重建方法兼容，并且可以提高其重建质量。