通过混合精度量化与CUDA内核融合，显著提升深度视觉里程计的运行效率。
DPVO-QAT++: Heterogeneous QAT and CUDA Kernel Fusion for High-Performance Deep Patch Visual Odometry
- 采用前端伪量化、后端全精度的异构精度设计，平衡速度与精度。
- 在TartanAir上实现52.1%帧率提升，内存占用减少64.9%。
- 适合部署于资源受限的自动驾驶设备，工程落地性强。
基于深度学习的视觉SLAM系统具备出色的几何推理能力，但其高昂的计算开销严重限制了在资源受限的自主平台上的部署。本文提出分层量化优化框架DPVO-QAT++，通过可学习缩放参数化、视觉里程计前后端异构精度设计（前端浮点伪量化，支持FP16/FP32；后端全精度）以及伪量化原生CUDA内核融合（自定义CUDA核函数），显著降低内存占用并提升处理速度，同时保持原始模型的轨迹精度。在TartanAir数据集上，平均帧率提升52.1%，中位延迟降低29.1%，峰值GPU内存占用减少64.9%，32个验证序列轨迹精度（ATE）与原版DPVO模型相当。在EuRoC数据集上，平均帧率提升30.1%，中位延迟降低23.1%，峰值内存减少37.7%，11个序列的轨迹精度保持一致。实验表明，DPVO-QAT++有效弥合高精度深度视觉里程计与实际部署效率需求之间的差距，为该技术在真实嵌入式平台的应用提供了可行的工程范式。
原文摘要 · Abstract (English)
Deep learning-based Visual SLAM (vSLAM) systems exhibit exceptional geometric reasoning capabilities, yet their prohibitive computational overhead severely restricts deployment on resource-constrained autonomous platforms. This paper presents a hierarchical quantization optimization framework, DPVO-QAT++ (DPVO-QAT++: Heterogeneous QAT and CUDA Kernel Fusion for High-Performance Deep Patch Visual Odometry). Through the synergistic integration of learnable scale parameterization, a heterogeneous precision design for the Visual Odometry (VO) front-end and back-end (front-end floating-point fake quantization with FP16/FP32; back-end full precision), and GPU-native kernel fusion for fake quantization (custom CUDA kernels), our framework significantly reduces memory footprint and increases processing speed while preserving the trajectory accuracy of the original model. On the TartanAir dataset, our framework achieves an average FPS increase of 52.1%, a 29.1% reduction in median latency, and a 64.9% reduction in peak GPU memory reservation, while maintaining trajectory accuracy (ATE) comparable to the original DPVO model across 32 validation sequences. On the EuRoC dataset, it realizes an average FPS increase of 30.1%, a 23.1% reduction in median latency, and a 37.7% reduction in peak GPU memory reservation, maintaining comparable trajectory accuracy (ATE) across 11 validation sequences. Experimental results demonstrate that DPVO-QAT++ effectively bridges the gap between high-precision deep VO and the efficiency requirements for practical deployment, offering a viable engineering paradigm for the application of this technology on real-world embedded platforms. Keywords: Visual Odometry, Heterogeneous Precision Architecture, Quantization-Aware Training, CUDA Kernel Fusion, Scale-Only Training, Deep Patch Visual Odometry, GPU-Native Kernel Fusion.
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