  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.
