19 citations · 23 across the 7 of their papers we have counts for
7 papers
LiDAR Prompted Spatio-Temporal Multi-View Stereo for Autonomous Driving
Qihao Sun, Jiarun Liu, Ziqian Ni +5
Accurate metric depth is critical for autonomous driving perception and simulation, yet current approaches struggle to achieve high metric accuracy, multi-view and temporal consist…
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator
Shiguang Wang, Tao Xie, Haijun Liu +2
Channel Pruning is one of the most widespread techniques used to compress deep neural networks while maintaining their performances. Currently, a typical pruning algorithm leverage…
FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer
Xinyu Zhang, Li Wang, Zhiqiang Jiang +6
Local Feature Matching, an essential component of several computer vision tasks (e.g., structure from motion and visual localization), has been effectively settled by Transformer-b…
OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes
Tao Xie, Kun Dai, Siyi Lu +7
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a uni…
Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking
Xiaoyu Li, Tao Xie, Dedong Liu +5
3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. Howe…
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
Kun Dai, Tao Xie, Ke Wang +3
Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behav…
DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching
Tao Xie, Kun Dai, Ke Wang +2
Local feature matching between images remains a challenging task, especially in the presence of significant appearance variations, e.g., extreme viewpoint changes. In this work, we…