Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research. The recent promise leverages neural implicit surface learning and differentiable volume rendering, and achieves both the recovery of scene geometry and synthesis of novel views, where deep priors of neural models are used as an inductive smoothness bias. While promising for object-level surfaces, these methods suffer when coping with complex scene surfaces. In the meanwhile, traditional multi-view stereo can recover the geometry of scenes with rich textures, by globally optimizing the local, pixel-wise correspondences across multiple views. We are thus motivated to make use of the complementary benefits from the two strategies, and propose a method termed Helix-shaped neural implicit Surface learning or HelixSurf; HelixSurf uses the intermediate prediction from one strategy as the guidance to regularize the learning of the other one, and conducts such intertwined regularization iteratively during the learning process. We also propose an efficient scheme for differentiable volume rendering in HelixSurf. Experiments on surface reconstruction of indoor scenes show that our method compares favorably with existing methods and is orders of magnitude faster, even when some of existing methods are assisted with auxiliary training data. The source code is available at https://github.com/Gorilla-Lab-SCUT/HelixSurf.

翻译：从多视角图像恢复场景几何结构是计算机视觉研究中的长期挑战。近期研究采用神经隐式曲面学习与可微体积渲染方法，通过深度模型先验作为归纳平滑性偏置，同时实现场景几何恢复与新视角合成。尽管此类方法在物体级曲面表现优异，但在处理复杂场景曲面时存在局限。与此同时，传统多视角立体视觉通过全局优化跨视图的局部像素级对应关系，能够恢复具有丰富纹理的场景几何。受此启发，我们融合两种策略的互补优势，提出名为螺旋形神经隐式曲面学习（HelixSurf）的方法。该方法将某一策略的中间预测结果作为另一策略学习的正则化引导，并在学习过程中迭代执行这种交织正则化。我们还为HelixSurf设计了高效的可微体积渲染方案。室内场景曲面重建实验表明，即使部分现有方法借助辅助训练数据，所提方法在性能上仍具竞争力且计算效率提升数个数量级。源代码见https://github.com/Gorilla-Lab-SCUT/HelixSurf。