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

翻译：神经三维场景重建方法在重建室内场景中复杂几何结构和低纹理区域时取得了显著效果。然而这些方法严重依赖三维数据，在现实世界中获取此类数据成本高昂且耗时。本文提出一种新颖的神经重建方法，在平面约束下利用稀疏深度重建场景，无需三维监督。我们引入符号距离函数场、颜色场和概率场来表示场景，通过可微分光线步进技术以可获取的二维图像作为监督信号优化这些场以实现重建。利用几何约束获得的稀疏深度提升了复杂几何场景区域的重建质量：该约束将表面三维点投影到不同二维图像中具有相似特征的相似区域。同时施加平面约束使大型平面与室内地面平行或垂直。这两类约束共同帮助重建场景精确且平滑的几何结构。在无需三维监督的条件下，本方法在ScanNet数据集上取得了与现有依赖三维监督的方法相媲美的性能。