14 citations · 48 across the 11 of their papers we have counts for
11 papers
Online3R: Online Learning for Consistent Sequential Reconstruction Based on Geometry Foundation Model
Shunkai Zhou, Zike Yan, Fei Xue +3
We present Online3R, a new sequential reconstruction framework that is capable of adapting to new scenes through online learning, effectively resolving inconsistency issues. Specif…
VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning
Tianxing Zhou, Feiyang Xue, Zhangchen Ye +3
Imitation learning is a prominent paradigm for robotic manipulation. However, existing visual imitation methods map 2D image observations directly to 3D action outputs, imposing a…
SFD2: Semantic-guided Feature Detection and Description
Fei Xue, Ignas Budvytis, Roberto Cipolla
Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant loc…
IMP: Iterative Matching and Pose Estimation with Adaptive Pooling
Fei Xue, Ignas Budvytis, Roberto Cipolla
Previous methods solve feature matching and pose estimation using a two-stage process by first finding matches and then estimating the pose. As they ignore the geometric relationsh…
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…
Guided Feature Selection for Deep Visual Odometry
Fei Xue, Qiuyuan Wang, Xin Wang +3
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks. Different from current monocular vis…