用视觉马尔可夫架构实现快速高精度视差图生成
ViM-Disparity: Bridging the Gap of Speed, Accuracy and Memory for Disparity Map Generation
- 基于视觉马尔可夫结构，平衡速度、精度与内存消耗
- 提出联合评估指标，同时衡量推理速度、计算开销和准确率
- 适合实时立体视觉应用，如自动驾驶与机器人导航
本文提出一种基于视觉马尔可夫（ViM）的架构，旨在解决实时、高精度视差图生成（DMG）模型在计算开销上的固有权衡。我们还设计了一种性能度量方法，可联合评估模型的推理速度、计算开销和准确性。代码与模型已在GitHub公开：https://github.com/MBora/ViM-Disparity。
原文摘要 · Abstract (English)
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.
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