Point-NeuS: Point-Guided Neural Implicit Surface Reconstruction by Volume Rendering

Recently, learning neural implicit surface by volume rendering has been a promising way for multi-view reconstruction. However, limited accuracy and excessive time complexity remain bottlenecks that current methods urgently need to overcome. To address these challenges, we propose a new method called Point-NeuS, utilizing point-guided mechanisms to achieve accurate and efficient reconstruction. Point modeling is organically embedded into the volume rendering to enhance and regularize the representation of implicit surface. Specifically, to achieve precise point guidance and noise robustness, aleatoric uncertainty of the point cloud is modeled to capture the distribution of noise and estimate the reliability of points. Additionally, a Neural Projection module connecting points and images is introduced to add geometric constraints to the Signed Distance Function (SDF). To better compensate for geometric bias between volume rendering and point modeling, high-fidelity points are filtered into an Implicit Displacement Network to improve the representation of SDF. Benefiting from our effective point guidance, lightweight networks are employed to achieve an impressive 11x speedup compared to NeuS. Extensive experiments show that our method yields high-quality surfaces, especially for fine-grained details and smooth regions. Moreover, it exhibits strong robustness to both noisy and sparse data.