PSDF: Prior-Driven Neural Implicit Surface Learning for Multi-view Reconstruction

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.

PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction

Neural surfaces learning has shown impressive performance in multi-view surface reconstruction. However, most existing methods use large multilayer perceptrons (MLPs) to train their models from scratch, resulting in hours of training for a single scene. Recently, how to accelerate the neural surfaces learning has received a lot of attention and remains an open problem. In this work, we propose a prior-based residual learning paradigm for fast multi-view neural surface reconstruction. This paradigm consists of two optimization stages. In the first stage, we propose to leverage generalization models to generate a basis signed distance function (SDF) field. This initial field can be quickly obtained by fusing multiple local SDF fields produced by generalization models. This provides a coarse global geometry prior. Based on this prior, in the second stage, a fast residual learning strategy based on hash-encoding networks is proposed to encode an offset SDF field for the basis SDF field. Moreover, we introduce a prior-guided sampling scheme to help the residual learning stage converge better, and thus recover finer structures. With our designed paradigm, experimental results show that our method only takes about 3 minutes to reconstruct the surface of a single scene, while achieving competitive surface quality. Our code will be released upon publication.

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.