PSDF: Prior-Driven Neural Implicit Surface Learning for Multi-view
  Reconstruction
Wanjuan Su Chen Zhang Qingshan Xu Wenbing Tao
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.