论文标题
自我监督的注意力学习深度和自我运动估计
Self-Supervised Attention Learning for Depth and Ego-motion Estimation
论文作者
论文摘要
我们从图像序列中解决了深度和自我运动估计的问题。该领域的最新进展建议使用图像重建的方式训练两项任务的深度学习模型。我们修改了当前方法的假设和局限性，并提出了两项改进，以提高深度和自我运动估计的性能。我们首先使用Lie Group属性来强制序列中图像及其重建之间的几何一致性。然后，我们提出了一种机制，以关注图像重建损坏的图像区域。我们展示了如何以管道中注意门的形式整合注意力机制，并将注意力系数用作掩模。我们评估了Kitti数据集中的新体系结构，并将其与以前的技术进行比较。我们表明，我们的方法改善了自我运动估计的最新结果，并获得了深度估计的可比结果。
We address the problem of depth and ego-motion estimation from image sequences. Recent advances in the domain propose to train a deep learning model for both tasks using image reconstruction in a self-supervised manner. We revise the assumptions and the limitations of the current approaches and propose two improvements to boost the performance of the depth and ego-motion estimation. We first use Lie group properties to enforce the geometric consistency between images in the sequence and their reconstructions. We then propose a mechanism to pay an attention to image regions where the image reconstruction get corrupted. We show how to integrate the attention mechanism in the form of attention gates in the pipeline and use attention coefficients as a mask. We evaluate the new architecture on the KITTI datasets and compare it to the previous techniques. We show that our approach improves the state-of-the-art results for ego-motion estimation and achieve comparable results for depth estimation.
