Given only a set of images, neural implicit surface representation has shown its capability in 3D surface reconstruction. However, as the nature of per-scene optimization is based on the volumetric rendering of color, previous neural implicit surface reconstruction methods usually fail in low-textured regions, including the floors, walls, etc., which commonly exist for indoor scenes. Being aware of the fact that these low-textured regions usually correspond to planes, without introducing additional ground-truth supervisory signals or making additional assumptions about the room layout, we propose to leverage a novel Pseudo Plane-regularized Signed Distance Field (P$^2$SDF) for indoor scene reconstruction. Specifically, we consider adjacent pixels with similar colors to be on the same pseudo planes. The plane parameters are then estimated on the fly during training by an efficient and effective two-step scheme. Then the signed distances of the points on the planes are regularized by the estimated plane parameters in the training phase. As the unsupervised plane segments are usually noisy and inaccurate, we propose to assign different weights to the sampled points on the plane in plane estimation as well as the regularization loss. The weights come by fusing the plane segments from different views. As the sampled rays in the planar regions are redundant, leading to inefficient training, we further propose a keypoint-guided rays sampling strategy that attends to the informative textured regions with large color variations, and the implicit network gets a better reconstruction, compared with the original uniform ray sampling strategy. Experiments show that our P$^2$SDF achieves competitive reconstruction performance in Manhattan scenes. Further, as we do not introduce any additional room layout assumption, our P$^2$SDF generalizes well to the reconstruction of non-Manhattan scenes.

翻译：仅给定一组图像，神经隐式表面表示在三维表面重建中已展现出其能力。然而，由于基于颜色体积渲染的每场景优化特性，先前的神经隐式表面重建方法通常在低纹理区域（包括室内场景中常见的地板、墙壁等）表现不佳。基于这些低纹理区域通常对应平面这一事实，在不引入额外真值监督信号或对房间布局作出额外假设的情况下，我们提出利用新颖的伪平面正则化符号距离场（P$^2$SDF）进行室内场景重建。具体而言，我们将颜色相近的相邻像素视为处于同一伪平面上。随后，在训练过程中通过高效有效的两步方案即时估计平面参数。在训练阶段，利用估计的平面参数对平面上点的符号距离进行正则化。鉴于无监督平面分割通常存在噪声且不准确，我们提出在平面估计及正则化损失中为平面上的采样点分配不同权重，该权重通过融合不同视角的平面分割获得。由于平面区域中的采样射线存在冗余导致训练效率低下，我们进一步提出关键点引导的射线采样策略，该策略关注具有大颜色变化的信息性纹理区域，与原始均匀射线采样策略相比，隐式网络能获得更好的重建效果。实验表明，我们的P$^2$SDF在曼哈顿场景中取得了具有竞争力的重建性能。此外，由于未引入任何额外房间布局假设，我们的P$^2$SDF能良好地泛化至非曼哈顿场景的重建。