FTSplat: Feed-forward Triangle Splatting Network
Xiong Jinlin, Li Can, Shen Jiawei, Qi Zhigang, Sun Lei, Zhao Dongyang

TL;DR
FTSplat introduces a fast, feed-forward approach to generate 3D triangle meshes directly from multi-view images, enabling real-time high-fidelity reconstruction suitable for simulation without per-scene optimization.
Contribution
The paper presents a novel feed-forward framework for triangle primitive generation that predicts continuous surfaces from images, improving efficiency and geometric consistency over existing methods.
Findings
Achieves real-time 3D reconstruction without per-scene optimization
Produces simulation-ready models compatible with standard tools
Maintains high geometric fidelity and stability
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topics3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques · Generative Adversarial Networks and Image Synthesis
