Neural implicit representations have revolutionized dense multi-view surface reconstruction, yet their performance significantly diminishes with sparse input views. A few pioneering works have sought to tackle the challenge of sparse-view reconstruction by leveraging additional geometric priors or multi-scene generalizability. However, they are still hindered by the imperfect choice of input views, using images under empirically determined viewpoints to provide considerable overlap. We propose PVP-Recon, a novel and effective sparse-view surface reconstruction method that progressively plans the next best views to form an optimal set of sparse viewpoints for image capturing. PVP-Recon starts initial surface reconstruction with as few as 3 views and progressively adds new views which are determined based on a novel warping score that reflects the information gain of each newly added view. This progressive view planning progress is interleaved with a neural SDF-based reconstruction module that utilizes multi-resolution hash features, enhanced by a progressive training scheme and a directional Hessian loss. Quantitative and qualitative experiments on three benchmark datasets show that our framework achieves high-quality reconstruction with a constrained input budget and outperforms existing baselines.

翻译：神经隐式表示已经彻底改变了密集多视图表面重建，但其性能在输入视图稀疏时会显著下降。一些开创性工作试图通过利用额外的几何先验或多场景泛化能力来应对稀疏视图重建的挑战。然而，它们仍然受限于输入视图的不完美选择，即使用经验确定的视角下的图像来提供足够的重叠区域。我们提出了PVP-Recon，一种新颖且有效的稀疏视图表面重建方法，它通过渐进式规划下一个最佳视图，以形成用于图像采集的最优稀疏视点集合。PVP-Recon从少至3个视图开始进行初始表面重建，并逐步添加新视图。新视图的确定基于一种新颖的扭曲分数，该分数反映了每个新增视图的信息增益。这一渐进式视图规划过程与一个基于神经符号距离场的重建模块交错进行，该模块利用了多分辨率哈希特征，并通过渐进式训练方案和方向性Hessian损失得到增强。在三个基准数据集上的定量和定性实验表明，我们的框架在有限的输入预算下实现了高质量的重建，并优于现有基线方法。