Neural scene reconstruction methods have achieved impressive performance in reconstructing complex geometry and low-textured regions in large scenes. However, these methods heavily rely on 3D supervised information which is costly and time-consuming to obtain in the real world. In this paper, we propose a novel neural reconstruction method that reconstructs scenes without 3D supervision. We perform differentiable volume rendering for scene reconstruction by using accessible 2D images as supervision. We impose geometry to improve the reconstruction quality of complex geometry regions in the scenes, and impose plane constraints to improve the reconstruction quality of low-textured regions in the scenes. Specifically, we introduce a signed distance function (SDF) field, a color field, and a probability field to represent the scene, and optimize the fields under the differentiable ray marching to reconstruct the scene. Besides, we impose geometric constraints that project 3D points on the surface to similar-looking regions with similar features in different views. We also impose plane constraints to make large planes keep parallel or vertical to the wall or floor. These two constraints help to reconstruct accurate and smooth geometry structures of the scene. Without 3D supervision information, our method achieves competitive reconstruction compared with some existing methods that use 3D information as supervision on the ScanNet dataset.

翻译：神经场景重建方法在大型场景中复杂几何与低纹理区域的重建中取得了优异性能，但这类方法严重依赖三维监督信息——这种信息在现实世界中获取成本高昂且耗时。本文提出一种无需三维监督的新型神经重建方法。我们以易获取的二维图像作为监督信号，通过可微分体渲染实现场景重建。具体而言：1) 施加几何约束提升场景中复杂几何区域的重建质量；2) 引入平面约束改善低纹理区域的重建效果。我们采用符号距离函数场、颜色场与概率场联合表示场景，并通过可微分光线步进优化这些场以实现场景重建。此外，我们施加几何约束将表面三维点投影至不同视角中特征相似的对应区域，同时施加平面约束使大型平面与墙面/地板保持平行或垂直。这两种约束有助于重建场景精确且平滑的几何结构。在无需三维监督信息的条件下，本方法与ScanNet数据集上部分使用三维信息作为监督的现有方法相比，取得了具有竞争力的重建效果。