无需优化，单次前向计算生成可直接用于仿真的三角网格模型。
FTSplat: Feed-forward Triangle Splatting Network
- 基于校准多视角图像，直接预测连续三角面片，跳过传统优化流程。
- 在标准图形与机器人仿真器中实现无缝兼容，重建效率显著提升。
- 引入像素对齐的三角生成模块与3D点云监督，增强几何一致性与稳定性。
高保真三维重建对机器人和仿真至关重要。尽管神经辐射场（NeRF）与3D高斯溅射（3DGS）能实现出色的渲染质量，但其依赖耗时的场景级优化，限制了实时部署。新兴的前馈式高斯溅射方法提升了效率，却常缺乏直接用于仿真的显式流形几何结构。为此，我们提出一种前馈式三角基元生成框架，可从校准的多视角图像中直接预测连续三角面片。该方法通过一次前向传播生成仿真就绪的模型，避免了场景级优化与后处理。我们设计了像素对齐的三角生成模块，并引入相对3D点云监督，以增强几何学习的稳定性和一致性。实验表明，该方法在保持高效重建的同时，与标准图形和机器人仿真器具有无缝兼容性。
原文摘要 · Abstract (English)
High-fidelity three-dimensional (3D) reconstruction is essential for robotics and simulation. While Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) achieve impressive rendering quality, their reliance on time-consuming per-scene optimization limits real-time deployment. Emerging feed-forward Gaussian splatting methods improve efficiency but often lack explicit, manifold geometry required for direct simulation. To address these limitations, we propose a feed-forward framework for triangle primitive generation that directly predicts continuous triangle surfaces from calibrated multi-view images. Our method produces simulation-ready models in a single forward pass, obviating the need for per-scene optimization or post-processing. We introduce a pixel-aligned triangle generation module and incorporate relative 3D point cloud supervision to enhance geometric learning stability and consistency. Experiments demonstrate that our method achieves efficient reconstruction while maintaining seamless compatibility with standard graphics and robotic simulators.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。