CuRast: Cuda-Based Software Rasterization for Billions of Triangles

2026-04-23

AI Summary

This work proposes an efficient software rasterization method for dense, opaque meshes—comprising hundreds of millions to billions of triangles—as commonly encountered in photogrammetry and related applications, without requiring prebuilt acceleration structures. The approach employs a three-stage CUDA compute shader pipeline: small triangles are processed directly in the first stage using atomicMin operations to record the nearest fragments, while large triangles are deferred to subsequent stages. Compared to Vulkan hardware rasterization, the method achieves 2–5× speedup for single-instance scenes and up to 12× acceleration with instanced rendering, substantially outperforming existing solutions, although it remains approximately an order of magnitude slower on low-polygon-count meshes.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Previous work shows that small triangles can be rasterized efficiently with compute shaders. Building on this insight, we explore how far this can be pushed for massive triangle datasets without the need to construct acceleration structures in advance. Method: A 3-stage rasterization pipeline first rasterizes small triangles directly in stage 1, using atomicMin to store the closest fragments. Larger triangles are forwarded to stages 2 and 3. Results: CuRast can render models with hundreds of millions of triangles up to 2-5x (unique) or up to 12x (instanced) faster than Vulkan. Vulkan remains an order of magnitude faster for low-poly meshes. Limitations: We currently focus on dense, opaque meshes that you would typically obtain from photogrammetry/3D reconstruction. Blending/Transparency is not yet supported, and scenes with thousands of low-poly meshes are not implemented efficiently. Future Work: To make it suitable for games and a wider range of use cases, future work will need to (1) optimize handling of scenes with tens of thousands of nodes/meshes, (2) add support for hierarchical clustered LODs such as those produced by Meshoptimizer, (3) add support for transparency, likely in its own stage so as to keep opaque rasterization untouched and fast. Source Code: https://github.com/m-schuetz/CuRast

Problem

• software rasterization
• massive triangle datasets
• CUDA
• acceleration structures
• real-time rendering

Innovation

• CUDA-based rasterization
• massive triangle rendering
• compute shader rasterization
• atomicMin fragment storage
• software rasterization pipeline