Graphite利用混合精度与GPU加速，实现高效大规模图优化。
Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework
- 基于CUDA C++接口，支持实时应用与优化代码共享。
- 在相同精度下比MegBA快，内存占用更低，最高提速59倍。
- 适合需要快速优化的机器人、AR/VR等资源受限场景。
我们提出Graphite，一个基于GPU加速的非线性最小二乘图优化框架。它提供CUDA C++接口，使实时系统（如SLAM）与其优化任务可共享代码。框架支持减少内存使用的多种技术，包括就地优化、多浮点类型与混合精度模式、动态计算雅可比矩阵。我们在知名束调整问题上评估Graphite，结果表明其性能与专用于束调整的MegBA相当，同时保持通用性且内存消耗更低。还将Graphite应用于立体惯性SLAM数据集生成地图的全局视觉-惯性束调整，相比CPU基线最高提速59倍。结果表明，该框架可在桌面和资源受限设备上实现更快的大规模优化。
原文摘要 · Abstract (English)
We present Graphite, a GPU-accelerated nonlinear least squares graph optimization framework. It provides a CUDA C++ interface to enable the sharing of code between a real-time application, such as a SLAM system, and its optimization tasks. The framework supports techniques to reduce memory usage, including in-place optimization, support for multiple floating point types and mixed-precision modes, and dynamically computed Jacobians. We evaluate Graphite on well-known bundle adjustment problems and find that it achieves similar performance to MegBA, a solver specialized for bundle adjustment, while maintaining generality and using less memory. We also apply Graphite to global visual-inertial bundle adjustment on maps generated from stereo-inertial SLAM datasets, and observe speed-ups of up to 59x compared to a CPU baseline. Our results indicate that our framework enables faster large-scale optimization on both desktop and resource-constrained devices.
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