Triangle Splatting for Real-Time Radiance Field Rendering

2025-05-25

AI Summary

To address the inefficiency of voxel/implicit representations in neural rendering and the lack of graphics compatibility in explicit primitives (e.g., 3D Gaussians), this paper introduces Triangle Splatting—a novel differentiable rendering framework that employs triangles as the fundamental differentiable primitive for radiance field reconstruction and end-to-end mesh optimization. The method integrates differentiable splatting modeling, GPU-accelerated rasterization, and non-voxel explicit representation, enabling joint geometric and appearance optimization while preserving the computational efficiency of traditional rasterization. On the Mip-NeRF360 benchmark, Triangle Splatting achieves superior visual quality compared to contemporary non-voxel methods: it outperforms Zip-NeRF in PSNR on indoor scenes and attains over 2400 FPS (1280×720) on the Garden scene—significantly improving the speed–quality trade-off in novel-view synthesis.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

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 comeback. 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 extit{Garden} scene, we achieve over 2,400 FPS at 1280x720 resolution using an off-the-shelf mesh renderer. These results highlight the efficiency and effectiveness of triangle-based representations for high-quality novel view synthesis. Triangles bring us closer to mesh-based optimization by combining classical computer graphics with modern differentiable rendering frameworks. The project page is https://trianglesplatting.github.io/

Problem

• Develops differentiable triangle renderer for radiance fields
• Combines triangle efficiency with adaptive primitive density
• Achieves high fidelity and speed in rendering

Innovation

• Differentiable renderer optimizes triangles via gradients
• Renders triangles as differentiable splats for efficiency
• Combines classical graphics with modern rendering frameworks