TriaGS: 可微三角化引导的几何一致性用于3D高斯点扩散
TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting
AI总结：
TriaGS通过约束多视角三角化提升3D高斯点扩散的几何一致性，实现高保真表面重建。
摘要：
3D高斯点扩散对于实时生成新视角至关重要，因其高效性和能够渲染逼真图像的能力。然而，仅通过光度损失来构建3D高斯点会导致重建不一致。这种欠约束的过程常常导致"漂浮"伪影和无结构的几何，阻碍了高保真表面的提取。为了解决这个问题，本文介绍了一种新的方法，通过约束多视角三角化来提高重建质量。我们的方法旨在通过利用各种估计视角，在物理世界中达成3D表示的一致性。我们通过惩罚渲染的3D点与稳健共识点之间的偏差来优化这一过程，该共识点是通过自监督方式从一组邻近视角中重新三角化的。我们在多个数据集上展示了我们方法的有效性，取得了最先进的结果。在DTU数据集上，我们的方法实现了0.50毫米的平均切诺夫距离，优于其他显式方法。我们将开源我们的代码以促进社区验证并确保可重复性。
英文摘要：
3D Gaussian Splatting is crucial for real-time novel view synthesis due to its efficiency and ability to render photorealistic images. However, building a 3D Gaussian is guided solely by photometric loss, which can result in inconsistencies in reconstruction. This under-constrained process often results in "floater" artifacts and unstructured geometry, preventing the extraction of high-fidelity surfaces. To address this issue, our paper introduces a novel method that improves reconstruction by enforcing global geometry consistency through constrained multi-view triangulation. Our approach aims to achieve a consensus on 3D representation in the physical world by utilizing various estimated views. We optimize this process by penalizing the deviation of a rendered 3D point from a robust consensus point, which is re-triangulated from a bundle of neighboring views in a self-supervised fashion. We demonstrate the effectiveness of our method across multiple datasets, achieving state-of-the-art results. On the DTU dataset, our method attains a mean Chamfer Distance of 0.50 mm, outperforming comparable explicit methods. We will make our code open-source to facilitate community validation and ensure reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
