Recently, 3D Gaussian Splatting (3DGS) greatly accelerated mesh extraction from posed images due to its explicit representation and fast software rasterization. While the addition of geometric losses and other priors has improved the accuracy of extracted surfaces, mesh extraction remains difficult in scenes with abundant view-dependent effects. To resolve the resulting ambiguities, prior works rely on multi-view techniques, iterative mesh extraction, or large pre-trained models, sacrificing the inherent efficiency of 3DGS. In this work, we present a simple and efficient alternative by introducing a self-supervised confidence framework to 3DGS: within this framework, learnable confidence values dynamically balance photometric and geometric supervision. Extending our confidence-driven formulation, we introduce losses which penalize per-primitive color and normal variance and demonstrate their benefits to surface extraction. Finally, we complement the above with an improved appearance model, by decoupling the individual terms of the D-SSIM loss. Our final approach delivers state-of-the-art results for unbounded meshes while remaining highly efficient.

翻译：近年来，三维高斯泼溅（3DGS）凭借其显式表示和快速软件光栅化，极大地加速了从有姿态图像中提取网格的过程。尽管几何损失与先验知识的引入提升了提取表面的精度，但在包含丰富视角依赖效应的场景中，网格提取仍然困难重重。为解决由此产生的歧义性，现有工作依赖于多视角技术、迭代网格提取或大型预训练模型，牺牲了3DGS固有的高效性。本文提出一种简单高效的替代方案：将自监督置信度框架引入3DGS。在该框架中，可学习的置信度值动态平衡光度监督与几何监督。基于置信度驱动的公式，我们进一步引入惩罚每个原语颜色和法向方差的新损失函数，并论证其对表面提取的益处。最后，通过解耦D-SSIM损失的各个分量，我们改进了外观模型。所提方法在保持高效性的同时，在无界网格提取任务中取得了最先进的结果。