Neural surface reconstruction methods typically treat camera poses as fixed values, assuming perfect accuracy from Structure-from-Motion (SfM) systems. This assumption breaks down with imperfect pose estimates, leading to distorted or incomplete reconstructions. We present PCM-NeRF, a probabilistic framework that augments neural surface reconstruction with per-camera learnable uncertainty, built on top of SG-NeRF. Rather than treating all cameras equally throughout optimization, we represent each pose as a distribution with a learnable mean and variance, initialized from SfM correspondence quality. An uncertainty regularization loss couples the learned variance to view confidence, and the resulting uncertainty directly modulates the effective pose learning rate: uncertain cameras receive damped gradient updates, preventing poorly initialized views from corrupting the reconstruction. This lightweight mechanism requires no changes to the rendering pipeline and adds negligible overhead. Experiments on challenging scenes with severe pose outliers demonstrate that PCM-NeRF consistently outperforms state-of-the-art methods in both Chamfer Distance and F-Score, particularly for geometrically complex structures, without requiring foreground masks.

翻译：神经表面重建方法通常将相机姿态视为固定值，假定来自运动恢复结构（SfM）系统的完美精度。该假设在姿态估计不完善时失效，导致重建结果畸变或不完整。我们提出PCM-NeRF——一种基于SG-NeRF的概率框架，通过为每台相机引入可学习的感知不确定性来增强神经表面重建。不同于优化过程中对所有相机一视同仁，我们将每个姿态表示为具有可学习均值和方差的分布，并利用SfM对应质量进行初始化。不确定性正则化损失将学习到的方差与视图置信度相关联，所得不确定性直接调节有效姿态学习率：不确定性高的相机获得衰减梯度更新，防止初始化质量差的视图破坏重建。这种轻量级机制无需改变渲染管线，且引入可忽略的额外开销。在具有严重姿态离群值的复杂场景实验表明，PCM-NeRF在倒角距离和F-Score上持续优于最先进方法，尤其对几何复杂结构表现突出，且无需前景掩码。