14 citations · 20 across the 3 of their papers we have counts for
7 papers
Line Flow based SLAM
Qiuyuan Wang, Zike Yan, Junqiu Wang +3
We propose a visual SLAM method by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While feature-based SLAM methods have achieved e…
Deep Visual Odometry with Adaptive Memory
Fei Xue, Xin Wang, Junqiu Wang +1
We propose a novel deep visual odometry (VO) method that considers global information by selecting memory and refining poses. Existing learning-based methods take the VO task as a…
Self-Supervised Deep Visual Odometry with Online Adaptation
Shunkai Li, Xin Wang, Yingdian Cao +3
Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer fro…
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
Shunkai Li, Fei Xue, Xin Wang +2
We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous m…
Local Supports Global: Deep Camera Relocalization with Sequence Enhancement
Fei Xue, Xin Wang, Zike Yan +3
We propose to leverage the local information in image sequences to support global camera relocalization. In contrast to previous methods that regress global poses from single image…
Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
Fei Xue, Xin Wang, Shunkai Li +3
Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem. In contrast, we present a VO framework by incorporating two additional components call…