三角形溅射用于实时辐射场渲染
Triangle Splatting for Real-Time Radiance Field Rendering
AI总结：
本文提出三角形溅射可微渲染器，直接优化三角形，结合三角形效率与独立基元密度，在Mip-NeRF360上超越高斯溅射，实现高保真、快速收敛和超2400 FPS实时渲染。
摘要：
计算机图形学领域被神经辐射场和3D高斯溅射等模型彻底改变，取代三角形成为摄影测量的主导表示。在本文中，我们主张三角形的回归。我们开发了一个可微渲染器，通过端到端梯度直接优化三角形。我们通过将每个三角形渲染为可微溅射来实现这一点，将三角形的效率与基于独立基元的表示的适应性密度相结合。与流行的2D和3D高斯溅射方法相比，我们的方法实现了更高的视觉保真度、更快的收敛速度和更高的渲染吞吐量。在Mip-NeRF360数据集上，我们的方法在视觉保真度上优于并发的非体积基元，并在室内场景中实现了比最先进的Zip-NeRF更高的感知质量。三角形简单，与标准图形栈和GPU硬件兼容，且效率极高：对于Garden场景，我们使用现成的网格渲染器在1280x720分辨率下实现了超过2400 FPS的帧率。这些结果突显了基于三角形的表示在高品质新视角合成中的效率和有效性。三角形通过将经典计算机图形学与现代可微渲染框架相结合，使我们更接近基于网格的优化。项目页面为https://trianglesplatting.github.io/。
英文摘要：
The field of computer graphics was revolutionized by models such as Neural Radiance Fields and 3D Gaussian Splatting, displacing triangles as the dominant representation for photogrammetry. In this paper, we argue for a triangle comeback. We develop a differentiable renderer that directly optimizes triangles via end-to-end gradients. We achieve this by rendering each triangle as differentiable splats, combining the efficiency of triangles with the adaptive density of representations based on independent primitives. Compared to popular 2D and 3D Gaussian Splatting methods, our approach achieves higher visual fidelity, faster convergence, and increased rendering throughput. On the Mip-NeRF360 dataset, our method outperforms concurrent non-volumetric primitives in visual fidelity and achieves higher perceptual quality than the state-of-the-art Zip-NeRF on indoor scenes. Triangles are simple, compatible with standard graphics stacks and GPU hardware, and highly efficient: for the \textit{Garden} scene, we achieve over 2,400 FPS at 1280x720 resolution using an off-the-shelf mesh renderer. These results highlight the efficiency and effectiveness of triangle-based representations for high-quality novel view synthesis. Triangles bring us closer to mesh-based optimization by combining classical computer graphics with modern differentiable rendering frameworks. The project page is https://trianglesplatting.github.io/
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。
