TinyStereo: A Tiny Coarse-to-Fine Framework for Vision-Based Depth Estimation on Embedded GPUs
提出一种结合非学习算法和轻量级超分辨率残差神经网络的立体匹配框架，在嵌入式Jetson AGX GPU上实现5.17%的低匹配错误率和51 fps的实时处理速度，适用于机器人视觉和自动驾驶等场景。
Stereo vision, a popular depth estimation technology in computing vision, finds wide-ranging applications in embedded systems, including robotics vision and autonomous driving. These applications demand both high accuracy and fast processing speeds. To address hardware limitations, most current embedded systems rely on nonlearning algorithms for fast matching, sacrificing accuracy. Some recent studies have explored using convolutional neural networks (CNNs) to improve matching accuracy, but the computational load of existing learning-based systems hampers real-world applicability. This article presents significant contributions: 1) a novel stereo matching framework that greatly enhances accuracy on real-time embedded platforms and 2) a two-pronged approach combining a nonlearning-based algorithm and a lightweight super-resolution residual neural network (sRRNet). The nonlearning-based algorithm yields a low-resolution disparity map, while the lightweight sRRNet generates a high-resolution disparity map. Experimental results on benchmark data demonstrate that the proposed method achieves a low matching error rate of 5.17% and a real-time processing speed of 51 fps using the embedded Jetson AGX GPU. The proposed method outperforms all existing real-time embedded systems.