Visual Localization Using Semantic Segmentation and Depth Prediction

Understand

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.