FTSplat: Feed-forward Triangle Splatting Network

• URL: http://arxiv.org/abs/2603.05932v1
• Date: Fri, 06 Mar 2026 06:04:09 GMT
• Title: FTSplat: Feed-forward Triangle Splatting Network
• Abstract summary: We propose a feed-forward framework for triangle primitive generation that directly predicts continuous triangle surfaces from calibrated multi-view images.<n>Our method produces simulation-ready models in a single forward pass, obviating the need for per-scene optimization or post-processing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 and consistency. Experiments demonstrate that our method achieves efficient reconstruction while maintaining seamless compatibility with standard graphics and robotic simulators.

Related papers

• FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene Generation [72.38727895659405]
  We introduce FLAT, which decodes triangle splats directly from video diffusion latents.<n>On standard benchmarks, FLAT achieves significantly better geometric accuracy compared to state-of-the-art feedforward baselines.<n>We show that a lightweight test-time refinement step converts the predicted triangle soup into a fully opaque, game-engine-ready representation.
  arXiv  Detail & Related papers  (2026-06-23T17:53:41Z)
• TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction [65.03268438396556]
  TriSplat is a feed-forward reconstruction network that represents scenes with oriented triangle primitives.<n>It exports simulation-ready mesh scenes from a single forward pass.<n>Experiments on RealEstate10K and DL3DV show that this representation produces more geometry-faithful reconstructions than Gaussian feed-forward baselines.
  arXiv  Detail & Related papers  (2026-05-25T17:59:53Z)
• Triangle Splatting+: Differentiable Rendering with Opaque Triangles [54.18495204764292]
  We introduce Triangle Splatting+, which directly optimize triangles within a differentiable splatting framework.<n>Our method surpasses prior splatting approaches in visual fidelity while remaining efficient and fast to training.<n>The resulting semi-connected meshes support downstream applications such as physics-based simulation or interactive walkthroughs.
  arXiv  Detail & Related papers  (2025-09-29T17:43:46Z)
• 2D Triangle Splatting for Direct Differentiable Mesh Training [4.161453036693641]
  2D Triangle Splatting (2DTS) is a novel method that replaces 3D Gaussian primitives with 2D triangle facelets.<n>By incorporating a compactness parameter into the triangle primitives, we enable direct training of photorealistic meshes.<n>Our approach produces reconstructed meshes with superior visual quality compared to existing mesh reconstruction methods.
  arXiv  Detail & Related papers  (2025-06-23T12:26:47Z)
• HORT: Monocular Hand-held Objects Reconstruction with Transformers [61.36376511119355]
  Reconstructing hand-held objects in 3D from monocular images is a significant challenge in computer vision.<n>We propose a transformer-based model to efficiently reconstruct dense 3D point clouds of hand-held objects.<n>Our method achieves state-of-the-art accuracy with much faster inference speed, while generalizing well to in-the-wild images.
  arXiv  Detail & Related papers  (2025-03-27T09:45:09Z)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)
• CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model [37.75256020559125]
  We present a high-fidelity feed-forward single image-to-3D generative model. We highlight the necessity of integrating geometric priors into network design. Our model delivers a high-fidelity textured mesh from an image in just 10 seconds, without any test-time optimization.
  arXiv  Detail & Related papers  (2024-03-08T04:25:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.