用三角化约束提升3D高斯点云的几何一致性，减少漂浮物和结构混乱。
TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting
- 通过多视角三角化自监督优化点位置，强制几何一致
- DTU数据集上平均切比雪夫距离达0.50毫米，优于现有显式方法
- 适合追求高精度三维重建的研究者与工业应用
3D高斯点云渲染因高效且能生成逼真图像，是实时新视图合成的关键技术。然而，其3D点构建仅依赖光度损失，导致重建不一致，常出现'漂浮物'和无结构几何，难以提取高保真表面。为此，本文提出一种新方法，通过受约束的多视角三角化增强全局几何一致性。该方法利用多个估计视图，在自监督框架下重新三角化邻近视图中的点，惩罚渲染点与鲁棒共识点之间的偏差。在多个数据集上验证了方法有效性，于DTU数据集上实现0.50毫米的平均切比雪夫距离，超越同类显式方法。代码将开源，以促进社区验证与可复现性。
原文摘要 · Abstract (English)
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.
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