二维三角形投射用于直接可微网格训练
2D Triangle Splatting for Direct Differentiable Mesh Training
AI总结：
本文提出2DTS方法，用二维三角形替代三维高斯体，形成离散网格结构并保持连续体积分的优点，实验表明其在视觉质量上与高斯方法相当，且更直接连接传统网格表示。
摘要：
可微渲染与3D高斯体在多视角图像中重建高保真3D场景方面表现强大。尽管优于NeRF方法，但其在渲染速度和高级渲染效果如光照和阴影方面仍不如网格模型。本文提出2DTriangle Splatting（2DTS），用二维三角形体替代3D高斯体，自然形成离散网格结构并保留连续体积分的优势。通过控制紧凑性参数的结合和退火，我们的方法在训练过程中保持可微性，最终优化时生成完全不透明的三角形网格，无需额外后处理。实验结果表明，基于三角形的表示在视觉质量上与高斯方法竞争，同时提供更直接连接传统网格表示的途径。我们的方法弥合了可微渲染与传统网格渲染之间的差距，为需要可渲染网格重建的应用提供了有前景的解决方案。请访问我们的项目页面：https://gaoderender.github.io/triangle-splatting。
英文摘要：
Differentiable rendering with 3D Gaussian primitives has emerged as a powerful method for reconstructing high-fidelity 3D scenes from multi-view images. While it offers improvements over NeRF-based methods, this representation still encounters challenges with rendering speed and advanced rendering effects, such as relighting and shadow rendering, compared to mesh-based models. In this paper, we propose 2D Triangle Splatting (2DTS), a novel method that replaces 3D Gaussian primitives with 2D triangle primitives. This representation naturally forms a discrete mesh-like structure while retaining the benefits of continuous volumetric modeling. Through the incorporation and controlled annealing of a compactness parameter, our method maintains differentiability during training while producing triangle meshes with fully opaque faces at the end of optimization without the need for additional post-processing. Experimental results demonstrate that our triangle-based representation achieves competitive visual quality with Gaussian-based methods while providing a more direct bridge to mesh-based representations. Our method bridges the gap between differentiable rendering and traditional mesh-based rendering, offering a promising solution for applications requiring renderable mesh-like reconstructions. Please visit our project page at https://gaoderender.github.io/triangle-splatting.
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。
