基于Token-Sharing Transformer的轻量化单目深度估计
Lightweight Monocular Depth Estimation via Token-Sharing Transformer
AI总结：
针对Transformer参数大、计算成本高难以部署在嵌入式设备的问题，提出面向嵌入式设备优化的Token-Sharing Transformer轻量化单目深度估计架构，通过全局令牌共享实现高吞吐量高精度预测，在NYU Depth v2数据集上性能与速度优于现有方法。
摘要：
深度估计是各类机器人系统及应用中的一项重要任务。在移动机器人系统中，单目深度估计颇具应用价值，因为单个RGB摄像头部署成本低且体积小巧。鉴于这类需求显著且不断增长，已有众多面向移动机器人系统的轻量化单目深度估计网络被提出。尽管大多数轻量化单目深度估计方法都基于卷积神经网络开发，但近年来Transformer也逐渐被应用于单目深度估计领域。然而，Transformer参数量庞大、计算成本高，阻碍了其在嵌入式设备上的部署。本文提出了Token-Sharing Transformer（TST，令牌共享Transformer），这是一种采用Transformer的单目深度估计架构，专门针对嵌入式设备进行了优化。所提出的TST采用全局令牌共享机制，使模型能够在嵌入式设备上以高吞吐量实现准确的深度预测。实验结果表明，TST的性能优于现有的轻量化单目深度估计方法。在NYU Depth v2数据集上，TST在NVIDIA Jetson nano上的深度图生成速度可达63.4 FPS，在NVIDIA Jetson TX2上可达142.6 FPS，且误差低于现有方法。此外，TST在Jetson TX2上实现了高分辨率图像的实时深度估计，取得了具有竞争力的结果。
英文摘要：
Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera can be deployable at a low cost and compact size. Due to its significant and growing needs, many lightweight monocular depth estimation networks have been proposed for mobile robotics systems. While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently. However, massive parameters and large computational costs in the Transformer disturb the deployment to embedded devices. In this paper, we present a Token-Sharing Transformer (TST), an architecture using the Transformer for monocular depth estimation, optimized especially in embedded devices. The proposed TST utilizes global token sharing, which enables the model to obtain an accurate depth prediction with high throughput in embedded devices. Experimental results show that TST outperforms the existing lightweight monocular depth estimation methods. On the NYU Depth v2 dataset, TST can deliver depth maps up to 63.4 FPS in NVIDIA Jetson nano and 142.6 FPS in NVIDIA Jetson TX2, with lower errors than the existing methods. Furthermore, TST achieves real-time depth estimation of high-resolution images on Jetson TX2 with competitive results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
