DITTO: Dual and Integrated Latent Topologies for Implicit 3D
  Reconstruction
Jaehyeok Shim Kyungdon Joo
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.