Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian

• URL: http://arxiv.org/abs/2405.19657v1
• Date: Thu, 30 May 2024 03:18:30 GMT
• Title: Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian
• Authors: Wei Sun, Qi Zhang, Yanzhao Zhou, Qixiang Ye, Jianbin Jiao, Yuan Li,
• Abstract summary: 3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. Previous approaches have incorporated depth supervision into the training of 3D Gaussians to mitigate overfitting. We introduce a novel method to supervise the depth distribution of 3D Gaussians, utilizing depth priors with integrated uncertainty estimates.
• Score: 49.21866794516328
• License: http://creativecommons.org/licenses/by/4.0/
• 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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