Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian
CoRR(2024)
Abstract
3D Gaussian splatting has demonstrated impressive performance in real-time
novel view synthesis. However, achieving successful reconstruction from RGB
images generally requires multiple input views captured under static
conditions. To address the challenge of sparse input views, previous approaches
have incorporated depth supervision into the training of 3D Gaussians to
mitigate overfitting, using dense predictions from pretrained depth networks as
pseudo-ground truth. Nevertheless, depth predictions from monocular depth
estimation models inherently exhibit significant uncertainty in specific areas.
Relying solely on pixel-wise L2 loss may inadvertently incorporate detrimental
noise from these uncertain areas. In this work, we introduce a novel method to
supervise the depth distribution of 3D Gaussians, utilizing depth priors with
integrated uncertainty estimates. To address these localized errors in depth
predictions, we integrate a patch-wise optimal transport strategy to complement
traditional L2 loss in depth supervision. Extensive experiments conducted on
the LLFF, DTU, and Blender datasets demonstrate that our approach, UGOT,
achieves superior novel view synthesis and consistently outperforms
state-of-the-art methods.
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