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复杂几何结构和大场景低纹理区域的精确重建中取得了显著成效。然而，这类方法严重依赖三维监督信息，在现实世界中获取此类信息成本高昂且耗时。本文提出一种无需三维监督的新型神经重建方法。我们通过可访问的二维图像作为监督信号，对场景重建执行可微分体渲染。通过施加几何约束提升场景中复杂几何区域的重建质量，同时引入平面约束改善低纹理区域的重建效果。具体而言，我们构建符号距离函数场、颜色场和概率场联合表征场景，并在可微分射线行进框架下优化这些场以实现场景重建。此外，我们施加几何约束，将三维表面点投影到不同视角中具有相似特征的对应区域；同时引入平面约束，使大面积平面保持与墙壁或地板的平行/垂直关系。这两种约束有助于重建场景中精确且平滑的几何结构。在ScanNet数据集上的实验表明，即便缺乏三维监督信息，本方法仍能达到与部分使用三维信息作为监督的现有方法相当的重建性能。