  The reconstruction of object surfaces from multi-view images or monocular
video is a fundamental issue in computer vision. However, much of the recent
research concentrates on reconstructing geometry through implicit or explicit
methods. In this paper, we shift our focus towards reconstructing mesh in
conjunction with color. We remove the view-dependent color from neural volume
rendering while retaining volume rendering performance through a relighting
network. Mesh is extracted from the signed distance function (SDF) network for
the surface, and color for each surface vertex is drawn from the global color
network. To evaluate our approach, we conceived a in hand object scanning task
featuring numerous occlusions and dramatic shifts in lighting conditions. We've
gathered several videos for this task, and the results surpass those of any
existing methods capable of reconstructing mesh alongside color. Additionally,
our method's performance was assessed using public datasets, including DTU,
BlendedMVS, and OmniObject3D. The results indicated that our method performs
well across all these datasets. Project page:
https://colmar-zlicheng.github.io/color_neus.
