DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions

TL;DR

DySurface is a novel framework that combines explicit Gaussian representations with implicit SDFs to achieve consistent, high-quality 4D surface reconstruction in dynamic scenes, addressing geometric ambiguities of previous methods.

Contribution

It introduces a bridging approach that leverages deformed Gaussians to regularize implicit SDFs, improving temporal consistency and geometric accuracy in dynamic scene reconstruction.

Findings

Outperforms state-of-the-art methods in geometric accuracy metrics.

Produces watertight, detailed surface reconstructions.

Maintains competitive rendering performance.

Abstract

While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains a challenge. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) offer powerful dynamic scene rendering capabilities; however, relying solely on photometric optimization often leads to geometric ambiguities. This results in discontinuous surfaces, severe artifacts, and broken surfaces over time. To address these limitations, we present DySurface, a novel framework that bridges the effectiveness of explicit Gaussians with the geometric fidelity of implicit Signed Distance Functions (SDFs) in dynamic scenes. Our approach tackles the structural discrepancy between the forward deformation of 3DGS ($canonical \rightarrow dynamic$) and the backward deformation required for volumetric SDF rendering ($dynamic \rightarrow canonical$). Specifically, we propose the VoxGS-DSDF branch that leverages deformed Gaussians to construct a dynamic sparse voxel grid, providing explicit geometric guidance to the implicit SDF field. This explicit anchoring effectively regularizes the volumetric rendering process, significantly improving surface reconstruction quality, with watertight boundaries and detailed representations. Quantitative and qualitative experiments demonstrate that DySurface significantly outperforms state-of-the-art baselines in geometric accuracy metrics while maintaining competitive rendering performance.