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.

我们提出了一种新颖的双重和集成的潜在拓扑结构 (DITTO)，用于从嘈杂和稀疏的点云中进行隐式三维重建，通过同时利用点和网格潜在结构 (即，双重潜在) 来增强其优势，即网格潜在结构的稳定性和点潜在结构的详细描述能力。具体地说，DITTO 包括双重潜在编码器和集成的隐式解码器，在双重潜在编码器中，双重潜在层是构成编码器的关键模块，同时并行地优化两个潜在结构，保持它们的不同形状并实现递归交互。值得注意的是，双重潜在层内的新颖稀疏点变换器有效地优化点潜在结构。然后，集成的隐式解码器系统地组合这些优化后的潜在结构，实现高保真的三维重建，并在物体和场景级别的数据集上超过了先前的最先进方法，特别是在细且详细的结构上。

DITTO：隐式三维重建的双重与集成潜空间拓扑