23 citations · 80 across the 38 of their papers we have counts for
14 papers · 1 filter
Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning
Jiawei Sun, Bin Zhao, Dong Wang +5
Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness. However, the comput…
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
Chi Yan, Delin Qu, Dan Xu +4
In this paper, we introduce \textbf{GS-SLAM} that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better bala…
Implicit Event-RGBD Neural SLAM
Delin Qu, Chi Yan, Dong Wang +4
Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting v…
Affordance-Driven Next-Best-View Planning for Robotic Grasping
Xuechao Zhang, Dong Wang, Sun Han +7
Grasping occluded objects in cluttered environments is an essential component in complex robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View…
Robust Quadrupedal Locomotion via Risk-Averse Policy Learning
Jiyuan Shi, Chenjia Bai, Haoran He +6
The robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. Recently, Reinforcement Learning (RL) has shown promising results in legged locomotio…
Disentangled Contrastive Image Translation for Nighttime Surveillance
Guanzhou Lan, Bin Zhao, Xuelong Li
Nighttime surveillance suffers from degradation due to poor illumination and arduous human annotations. It is challengable and remains a security risk at night. Existing methods re…
On the Value of Myopic Behavior in Policy Reuse
Kang Xu, Chenjia Bai, Shuang Qiu +5
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or…
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Haoran He, Chenjia Bai, Kang Xu +5
Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are a…
Cross-Domain Policy Adaptation via Value-Guided Data Filtering
Kang Xu, Chenjia Bai, Xiaoteng Ma +5
Generalizing policies across different domains with dynamics mismatch poses a significant challenge in reinforcement learning. For example, a robot learns the policy in a simulator…
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Weichuang Li, Longhao Zhang, Dong Wang +7
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely…
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
Yihao Wang, Zhigang Wang, Bin Zhao +3
Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely…
Fully Self-Supervised Depth Estimation from Defocus Clue
Haozhe Si, Bin Zhao, Dong Wang +4
Depth-from-defocus (DFD), modeling the relationship between depth and defocus pattern in images, has demonstrated promising performance in depth estimation. Recently, several self-…
Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction
Delin Qu, Yizhen Lao, Zhigang Wang +3
This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion. Existing methods suffer from two main drawbacks. Firs…
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
Zoey Guo, Yiwen Tang, Ray Zhang +4
Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view c…