  Recently, learning multi-view neural surface reconstruction with the
supervision of point clouds or depth maps has been a promising way. However,
due to the underutilization of prior information, current methods still
struggle with the challenges of limited accuracy and excessive time complexity.
In addition, prior data perturbation is also an important but rarely considered
issue. To address these challenges, we propose a novel point-guided method
named PG-NeuS, which achieves accurate and efficient reconstruction while
robustly coping with point noise. Specifically, aleatoric uncertainty of the
point cloud is modeled to capture the distribution of noise, leading to noise
robustness. Furthermore, a Neural Projection module connecting points and
images is proposed to add geometric constraints to implicit surface, achieving
precise point guidance. To better compensate for geometric bias between volume
rendering and point modeling, high-fidelity points are filtered into a Bias
Network to further improve details representation. Benefiting from the
effective point guidance, even with a lightweight network, the proposed PG-NeuS
achieves fast convergence with an impressive 11x speedup compared to NeuS.
Extensive experiments show that our method yields high-quality surfaces with
high efficiency, especially for fine-grained details and smooth regions,
outperforming the state-of-the-art methods. Moreover, it exhibits strong
robustness to noisy data and sparse data.
