In this work we propose a Visual Mamba (ViM) based architecture, to dissolve the existing trade-off for real-time and accurate model with low computation overhead for disparity map generation (DMG). Moreover, we proposed a performance measure that can jointly evaluate the inference speed, computation overhead and the accurateness of a DMG model. The code implementation and corresponding models are available at: https://github.com/MBora/ViM-Disparity.

翻译：在本工作中，我们提出了一种基于视觉Mamba（ViM）的架构，旨在为视差图生成（DMG）任务化解现有实时、高精度与低计算开销模型之间的权衡。此外，我们提出了一种性能评估指标，能够联合评估DMG模型的推理速度、计算开销与准确性。代码实现及相应模型可在以下网址获取：https://github.com/MBora/ViM-Disparity。