3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction
Noda, Takeshi, Liu, Yu-Shen, Han, Zhizhong
原文摘要
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 constraining the movement of 3D Gaussians, aiming for more accurate depth rendering. Our self-constrained prior is a TSDF grid fused by the rendered depth during the learning of 3D Gaussians. The prior measures a band on both sides of the estimated surface for imposing more specific constraints on the right 3D Gaussians, such as removing 3D Gaussians outside the band, encouraging larger opacity for Gaussians near the center of the band or smaller opacity for Gaussians near the boundary of the band. We regularly update the prior by fusing more recent depth images which are usually more accurate, and progressively narrow the band to tighten the constraint on Gaussian movements. We justify our idea and report our superiority over the state-of-the-art methods in evaluations on widely used benchmarks.
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表