Spacetime Surface Regularization for Neural Dynamic Scene Reconstruction

Abstract

We propose an algorithm, 4DRegSDF, for the spacetime surface regularization to improve the fidelity of neural rendering and reconstruction in dynamic scenes. The key idea is to impose local rigidity on the deformable Signed Distance Function (SDF) for temporal coherency. Our approach works by (1) sampling points on the deformed surface by taking gradient steps toward the steepest direction along SDF, (2) extracting differential surface geometry, such as tangent plane or curvature, at each sample, and (3) adjusting the local rigidity at different timestamps. This enables our dynamic surface regularization to align 4D spacetime geometry via 3D canonical space more accurately. Experiments demonstrate that our 4DRegSDF achieves state-of-the-art performance in both reconstruction and rendering quality over synthetic and real-world datasets.