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.

翻译：从多视角图像或单目视频中重建物体表面是计算机视觉中的基本问题。然而，近期研究多聚焦于通过隐式或显式方法重建几何形状。本文转而关注几何网格与颜色的联合重建：通过重光照网络移除神经体渲染中的视角依赖颜色，同时保持体渲染性能；从符号距离函数（SDF）网络提取表面网格，并从全局颜色网络为每个表面顶点赋予颜色。为评估所提方法，我们设计了一项手持物体扫描任务，该任务包含大量遮挡和剧烈的光照变化。针对该任务采集的多段视频实验表明，本方法在联合重建网格与颜色方面的性能超越所有现有方法。此外，我们在DTU、BlendedMVS和OmniObject3D等公开数据集上评估了方法效果，结果表明本方法在所有数据集上均表现优异。项目主页：https://colmar-zlicheng.github.io/color_neus。