3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction

TL;DR

This paper introduces a self-constrained prior based on TSDF to improve high fidelity surface reconstruction in 3D Gaussian Splatting, enhancing accuracy and rendering quality over existing methods.

Contribution

It proposes a novel self-constrained prior derived from a TSDF grid to better constrain 3D Gaussians, improving surface reconstruction in 3D Gaussian Splatting.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Provides more accurate and complete surface reconstructions.

Enables progressive narrowing of the constraint band for improved results.

Abstract

Rendering 3D surfaces has been revolutionized within the modeling of radiance fields through either 3DGS or NeRF. Although 3DGS has shown advantages over NeRF in terms of rendering quality or speed, there is still room for improvement in recovering high fidelity surfaces through 3DGS. To resolve this issue, we propose a self-constrained prior to constrain the learning of 3D Gaussians, aiming for more accurate depth rendering. Our self-constrained prior is derived from a TSDF grid that is obtained by fusing the depth maps rendered with current 3D Gaussians. The prior measures a distance field around the estimated surface, offering a band centered at the surface for imposing more specific constraints on 3D Gaussians, such as removing Gaussians outside the band, moving Gaussians closer to the surface, and encouraging larger or smaller opacity in a geometry-aware manner. More importantly, our prior can be regularly updated by the most recent depth images which are usually more accurate and complete. In addition, the prior can also progressively narrow the band to tighten the imposed constraints. We justify our idea and report our superiority over the state-of-the-art methods in evaluations on widely used benchmarks.