Implicit Neural Surface Deformation with Explicit Velocity Fields
Lu Sang Zehranaz Canfes Dongliang Cao Florian Bernard Daniel Cremers
In this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propose to model the point movement using an explicit velocity field and directly deform a time-varying implicit field using the modified level-set equation. This equation utilizes an iso-surface evolution with Eikonal constraints in a compact formulation, ensuring the integrity of the signed distance field. By applying a smooth, volume-preserving constraint to the velocity field, our method successfully recovers physically plausible intermediate shapes. Our method is able to handle both rigid and non-rigid deformations without any intermediate shape supervision. Our experimental results demonstrate that our method significantly outperforms existing works, delivering superior results in both quality and efficiency.
Implicit Shape Representation, Neural Implicit Representation, Shape Deformation, Neural Surfaces
We propose a novel method that can simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds, and reconstruct physically plausible intermediate shapes without supervision.