14 citations · 34 across the 4 of their papers we have counts for
7 papers · 1 filter
Online Learning of a Probabilistic and Adaptive Scene Representation
Zike Yan, Xin Wang, Hongbin Zha
Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action. In this paper, we represent the scene w…
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…
ACE: Adapting to Changing Environments for Semantic Segmentation
Zuxuan Wu, Xin Wang, Joseph E. Gonzalez +2
Deep neural networks exhibit exceptional accuracy when they are trained and tested on the same data distributions. However, neural classifiers are often extremely brittle when conf…
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…