  Recent advancements in Neural Surface Reconstruction (NSR) have significantly
improved multi-view reconstruction when coupled with volume rendering. However,
relying solely on photometric consistency in image space falls short of
addressing complexities posed by real-world data, including occlusions and
non-Lambertian surfaces. To tackle these challenges, we propose an
investigation into feature-level consistent loss, aiming to harness valuable
feature priors from diverse pretext visual tasks and overcome current
limitations. It is crucial to note the existing gap in determining the most
effective pretext visual task for enhancing NSR. In this study, we
comprehensively explore multi-view feature priors from seven pretext visual
tasks, comprising thirteen methods. Our main goal is to strengthen NSR training
by considering a wide range of possibilities. Additionally, we examine the
impact of varying feature resolutions and evaluate both pixel-wise and
patch-wise consistent losses, providing insights into effective strategies for
improving NSR performance. By incorporating pre-trained representations from
MVSFormer and QuadTree, our approach can generate variations of MVS-NeuS and
Match-NeuS, respectively. Our results, analyzed on DTU and EPFL datasets,
reveal that feature priors from image matching and multi-view stereo outperform
other pretext tasks. Moreover, we discover that extending patch-wise
photometric consistency to the feature level surpasses the performance of
pixel-wise approaches. These findings underscore the effectiveness of these
techniques in enhancing NSR outcomes.
