用非因果状态空间实现实时高精度光流与视差估计
DensePercept-NCSSD: Vision Mamba towards Real-time Dense Visual Perception with Non-Causal State Space Duality
- 基于非因果Mamba块，融合双图输入实现高效感知
- 推理速度更快，显存占用低，保持高精度
- 适合实时3D密集感知场景，如自动驾驶
本文提出一种基于非因果选择性状态空间的密集视觉感知模型，用于实时精确的光流与视差估计。通过在非因果状态下空间中融合成对输入图像，所提模型兼具高速度与高效率，显著降低推理时间与GPU占用，同时保持高精度。实验结果与真实场景验证表明，该模型可有效支持统一的实时、高精度3D密集感知任务。代码与模型开源于https://github.com/vimstereo/DensePerceptNCSSD。
原文摘要 · Abstract (English)
In this work, we propose an accurate and real-time optical flow and disparity estimation model by fusing pairwise input images in the proposed non-causal selective state space for dense perception tasks. We propose a non-causal Mamba block-based model that is fast and efficient and aptly manages the constraints present in a real-time applications. Our proposed model reduces inference times while maintaining high accuracy and low GPU usage for optical flow and disparity map generation. The results and analysis, and validation in real-life scenario justify that our proposed model can be used for unified real-time and accurate 3D dense perception estimation tasks. The code, along with the models, can be found at https://github.com/vimstereo/DensePerceptNCSSD
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