Triangle Splatting+: Differentiable Rendering with Opaque Triangles

• URL: http://arxiv.org/abs/2509.25122v1
• Date: Mon, 29 Sep 2025 17:43:46 GMT
• Title: Triangle Splatting+: Differentiable Rendering with Opaque Triangles
• Authors: Jan Held, Renaud Vandeghen, Sanghyun Son, Daniel Rebain, Matheus Gadelha, Yi Zhou, Ming C. Lin, Marc Van Droogenbroeck, Andrea Tagliasacchi,
• Abstract summary: 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.
• Score: 54.18495204764292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing 3D scenes and synthesizing novel views has seen rapid progress in recent years. Neural Radiance Fields demonstrated that continuous volumetric radiance fields can achieve high-quality image synthesis, but their long training and rendering times limit practicality. 3D Gaussian Splatting (3DGS) addressed these issues by representing scenes with millions of Gaussians, enabling real-time rendering and fast optimization. However, Gaussian primitives are not natively compatible with the mesh-based pipelines used in VR headsets, and real-time graphics applications. Existing solutions attempt to convert Gaussians into meshes through post-processing or two-stage pipelines, which increases complexity and degrades visual quality. In this work, we introduce Triangle Splatting+, which directly optimizes triangles, the fundamental primitive of computer graphics, within a differentiable splatting framework. We formulate triangle parametrization to enable connectivity through shared vertices, and we design a training strategy that enforces opaque triangles. The final output is immediately usable in standard graphics engines without post-processing. Experiments on the Mip-NeRF360 and Tanks & Temples datasets show that Triangle Splatting+achieves state-of-the-art performance in mesh-based novel view synthesis. Our method surpasses prior splatting approaches in visual fidelity while remaining efficient and fast to training. Moreover, the resulting semi-connected meshes support downstream applications such as physics-based simulation or interactive walkthroughs. The project page is https://trianglesplatting2.github.io/trianglesplatting2/.

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