基于Transformer的单目视觉里程计模型：一种视频理解方法
Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach
AI总结：
本文提出TSformer-VO，一种基于时空自注意力的Transformer模型，将单目视觉里程计视为视频理解任务，端到端估计相机6自由度姿态，在KITTI数据集上取得最先进性能，优于DeepVO。
摘要：
从单个摄像头获取的图像中估计相机姿态是移动机器人和自动驾驶汽车中的一项传统任务。这个问题被称为单目视觉里程计，通常依赖于几何方法，这些方法需要针对特定场景进行大量的工程努力。深度学习方法已被证明在适当训练和大量可用数据的情况下具有泛化能力。基于Transformer的架构在自然语言处理和计算机视觉任务（如图像和视频理解）中主导了最先进水平。在这项工作中，我们将单目视觉里程计视为一个视频理解任务，以估计相机姿态的6个自由度。我们提出了TSformer-VO模型，该模型基于时空自注意力机制，从片段中提取特征并以端到端的方式估计运动。与基于几何和基于深度学习的方法相比，我们的方法在KITTI视觉里程计数据集上取得了具有竞争力的最先进性能，优于视觉里程计社区中广受接受的DeepVO实现。代码公开于https://github.com/aofrancani/TSformer-VO。
英文摘要：
Estimating the camera's pose given images from a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and often relies on geometric approaches that require considerable engineering effort for a specific scenario. Deep learning methods have been shown to be generalizable after proper training and with a large amount of available data. Transformer-based architectures have dominated the state-of-the-art in natural language processing and computer vision tasks, such as image and video understanding. In this work, we deal with the monocular visual odometry as a video understanding task to estimate the 6 degrees of freedom of a camera's pose. We contribute by presenting the TSformer-VO model based on spatio-temporal self-attention mechanisms to extract features from clips and estimate the motions in an end-to-end manner. Our approach achieved competitive state-of-the-art performance compared with geometry-based and deep learning-based methods on the KITTI visual odometry dataset, outperforming the DeepVO implementation highly accepted in the visual odometry community. The code is publicly available at https://github.com/aofrancani/TSformer-VO.
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。
