用更轻量的主干网络实现快速立体匹配，精度不降反升。
LeanStereo: A Leaner Backbone based Stereo Network
- 采用轻量化主干网络减少计算开销
- 用学习注意力加权代价体和LogL1损失提升精度
- 速度比顶尖方法快9至14倍，运算量少4倍
近期端到端深度立体匹配方法因性能优越而广受欢迎，但其计算与内存带宽需求高，依赖专用硬件（如GPU），推理时间仍远超传统方法，限制了实际应用。为兼顾高精度与合理推理速度，本文提出一种快速端到端立体匹配方法。核心在于采用更轻量的主干网络以实现加速，并通过基于学习的注意力权重代价体结合LogL1损失来弥补性能下降。实验表明，该方法运算量仅为ACVNet、LEAStereo和CFNet等先进方法的1/4，推理速度提升9至14倍，同时保持相当的匹配精度。
原文摘要 · Abstract (English)
Recently, end-to-end deep networks based stereo matching methods, mainly because of their performance, have gained popularity. However, this improvement in performance comes at the cost of increased computational and memory bandwidth requirements, thus necessitating specialized hardware (GPUs); even then, these methods have large inference times compared to classical methods. This limits their applicability in real-world applications. Although we desire high accuracy stereo methods albeit with reasonable inference time. To this end, we propose a fast end-to-end stereo matching method. Majority of this speedup comes from integrating a leaner backbone. To recover the performance lost because of a leaner backbone, we propose to use learned attention weights based cost volume combined with LogL1 loss for stereo matching. Using LogL1 loss not only improves the overall performance of the proposed network but also leads to faster convergence. We do a detailed empirical evaluation of different design choices and show that our method requires 4x less operations and is also about 9 to 14x faster compared to the state of the art methods like ACVNet [1], LEAStereo [2] and CFNet [3] while giving comparable performance.
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