Surface reconstruction has traditionally relied on the Multi-View Stereo (MVS)-based pipeline, which often suffers from noisy and incomplete geometry. This is due to that although MVS has been proven to be an effective way to recover the geometry of the scenes, especially for locally detailed areas with rich textures, it struggles to deal with areas with low texture and large variations of illumination where the photometric consistency is unreliable. Recently, Neural Implicit Surface Reconstruction (NISR) combines surface rendering and volume rendering techniques and bypasses the MVS as an intermediate step, which has emerged as a promising alternative to overcome the limitations of traditional pipelines. While NISR has shown impressive results on simple scenes, it remains challenging to recover delicate geometry from uncontrolled real-world scenes which is caused by its underconstrained optimization. To this end, the framework PSDF is proposed which resorts to external geometric priors from a pretrained MVS network and internal geometric priors inherent in the NISR model to facilitate high-quality neural implicit surface learning. Specifically, the visibility-aware feature consistency loss and depth prior-assisted sampling based on external geometric priors are introduced. These proposals provide powerfully geometric consistency constraints and aid in locating surface intersection points, thereby significantly improving the accuracy and delicate reconstruction of NISR. Meanwhile, the internal prior-guided importance rendering is presented to enhance the fidelity of the reconstructed surface mesh by mitigating the biased rendering issue in NISR. Extensive experiments on the Tanks and Temples dataset show that PSDF achieves state-of-the-art performance on complex uncontrolled scenes.

翻译：传统上，曲面重建依赖于基于多视图立体视觉（MVS）的流程，该流程常常受到噪声和不完整几何形状的困扰。这是因为，尽管MVS已被证明是恢复场景几何形状的有效方法，尤其是对于纹理丰富的局部细节区域，但在处理低纹理和光照变化大的区域时，由于光度一致性不可靠，其表现不佳。近年来，神经隐式曲面重建（NISR）结合了曲面渲染和体渲染技术，并跳过了MVS作为中间步骤，成为克服传统流程局限性的有前途的替代方案。虽然NISR在简单场景上展示了令人印象深刻的结果，但由于其欠约束优化，从不受控的真实场景中恢复精细几何形状仍然具有挑战性。为此，提出了PSDF框架，该框架利用预训练MVS网络的外部几何先验和NISR模型内在的内部几何先验，以促进高质量的神经隐式曲面学习。具体而言，引入了基于外部几何先验的可见性感知特征一致性损失和深度先验辅助采样。这些提议提供了强大的几何一致性约束，并有助于定位曲面交点，从而显著提高NISR的准确性和精细重建。同时，提出了内部先导引导的重要性渲染，通过减轻NISR中的有偏渲染问题，增强了重建曲面网格的保真度。在Tanks and Temples数据集上的大量实验表明，PSDF在复杂的非受控场景中实现了最先进的性能。