We propose a novel concept of dual and integrated latent topologies (DITTO in short) for implicit 3D reconstruction from noisy and sparse point clouds. Most existing methods predominantly focus on single latent type, such as point or grid latents. In contrast, the proposed DITTO leverages both point and grid latents (i.e., dual latent) to enhance their strengths, the stability of grid latents and the detail-rich capability of point latents. Concretely, DITTO consists of dual latent encoder and integrated implicit decoder. In the dual latent encoder, a dual latent layer, which is the key module block composing the encoder, refines both latents in parallel, maintaining their distinct shapes and enabling recursive interaction. Notably, a newly proposed dynamic sparse point transformer within the dual latent layer effectively refines point latents. Then, the integrated implicit decoder systematically combines these refined latents, achieving high-fidelity 3D reconstruction and surpassing previous state-of-the-art methods on object- and scene-level datasets, especially in thin and detailed structures.

翻译：我们提出了一种新颖的双重与集成潜在拓扑（简称DITTO）概念，用于从噪声和稀疏点云进行隐式三维重建。现有方法大多主要关注单一潜在类型，例如点潜在或网格潜在。相比之下，所提出的DITTO同时利用点潜在和网格潜在（即双重潜在），以增强它们各自的优势：网格潜在的稳定性与点潜在的高细节能力。具体而言，DITTO由双重潜在编码器和集成隐式解码器构成。在双重潜在编码器中，作为编码器关键组成模块的双重潜在层并行优化两种潜在表示，保持其各自独特的形态并实现递归交互。值得注意的是，双重潜在层中新提出的动态稀疏点变换器能有效优化点潜在。随后，集成隐式解码器系统性地融合这些优化后的潜在表示，实现了高保真度的三维重建，并在物体级和场景级数据集上超越了以往的最先进方法，尤其在薄壁和精细结构方面表现突出。