Triangle Splatting for Real-Time Radiance Field Rendering *equal contribution Arxiv Code Code will be released soon. 2D Gaussian Splatting vs Triangle Splatting Triangle Splatting (Ours) 2D Gaussian Splatting Ground truth vs Triangle Splatting Triangle Splatting (Ours) Ground truth Triangle Splatting (Ours) 2D Gaussian Splatting Triangle Splatting (Ours) Ground truth Triangle Splatting (Ours) 2D Gaussian Splatting Triangle Splatting (Ours) Ground truth Triangle Splatting achieves high-quality novel view synthesis and fast rendering by representing scenes with triangles. In contrast, the inherent softness of Gaussian primitives often leads to blurring and a loss of fine details, for example, beneath the bench or at the room’s door, whereas Triangle Splatting preserves sharp edges and accurately captures fine details. Abstract 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 come back.. 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 Garden scene, we achieve over 2,400 FPS at 1280×720 resolution using an off-the-shelf mesh renderer. These results highlight the efficiency and effectiveness of triangle-...
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