论文标题
使用语义分割和深度预测的视觉定位
Visual Localization Using Semantic Segmentation and Depth Prediction
论文作者
论文摘要
在本文中，我们提出了利用语义和深度线索的单眼视觉定位管道。我们采用语义一致性评估来对图像检索结果进行排名，并采用实用的聚类技术来拒绝估计异常值。此外，我们证明了通过多个特征提取器组合实现的实质性提升。此外，通过将深度预测与深度神经网络一起使用，我们表明，确定并消除了大量错误匹配的关键点。拟议的管道在长期视觉定位基准2020上优于大多数现有方法。
In this paper, we propose a monocular visual localization pipeline leveraging semantic and depth cues. We apply semantic consistency evaluation to rank the image retrieval results and a practical clustering technique to reject estimation outliers. In addition, we demonstrate a substantial performance boost achieved with a combination of multiple feature extractors. Furthermore, by using depth prediction with a deep neural network, we show that a significant amount of falsely matched keypoints are identified and eliminated. The proposed pipeline outperforms most of the existing approaches at the Long-Term Visual Localization benchmark 2020.
