36 citations · 60 across the 14 of their papers we have counts for
16 papers
DPed-VLN: A Benchmark for Socially Compliant Vision-and-Language Navigation in Dynamic Pedestrian Environments
Haojie Dai, Xiangyi Wang, Liuyi Wang +5
Vision-and-language navigation (VLN) has advanced rapidly in static indoor environments, but robots operating in human-populated spaces must ground language while responding to mov…
A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation
Liuyi Wang, Kai Sheng, Zongtao He +8
Navigation is a fundamental capability of autonomous systems, yet most existing approaches rely on highly structured models and strong prior assumptions, limiting their robustness…
KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility
Zhiming Xu, Haodong Yang, Chengju Liu +2
Deep Reinforcement Learning (DRL) has shown promise for social navigation, yet its real-world deployment remains hindered by a persistent sim-to-real gap arising from simplified fi…
Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation
Zhiming Xu, Weitao Zhou, Xianghui Pan +4
Real-world dynamics shifts pose a critical challenge for reinforcement learning in robotics, as policies tightly coupled to nominal environments often fail catastrophically when ph…
P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation
Kai Sheng, Liuyi Wang, Haojie Dai +5
Vision-and-language navigation (VLN) requires an embodied agent to ground natural-language instructions into executable navigation actions in unseen environments. Existing zero-sho…
Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces
Jiayuan Du, Yuebing Song, Yiming Zhao +6
End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, a…
MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language Navigation
Liuyi Wang, Zongtao He, Mengjiao Shen +3
Despite the remarkable developments of recent large models in Embodied Artificial Intelligence (E-AI), their integration into robotics is hampered by their excessive parameter size…
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation
Mengtan Zhang, Yi Feng, Qijun Chen +1
There has been a recent surge of interest in learning to perceive depth from monocular videos in an unsupervised fashion. A key challenge in this field is achieving robust and accu…
Playing to Vision Foundation Model's Strengths in Stereo Matching
Chuang-Wei Liu, Qijun Chen, Rui Fan
Stereo matching has become a key technique for 3D environment perception in intelligent vehicles. For a considerable time, convolutional neural networks (CNNs) have remained the ma…
Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching
Zhiwei Huang, Yikang Zhang, Qijun Chen +1
LiDAR-camera extrinsic calibration (LCEC) is crucial for data fusion in intelligent vehicles. Offline, target-based approaches have long been the preferred choice in this field. Ho…
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving
Sicen Guo, Ziwei Long, Zhiyuan Wu +3
Despite the impressive performance achieved by data-fusion networks with duplex encoders for visual semantic segmentation, they become ineffective when spatial geometric data are n…
SM-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving
Zhiyuan Wu, Yi Feng, Chuang-Wei Liu +3
Semantic segmentation and stereo matching are two essential components of 3D environmental perception systems for autonomous driving. Nevertheless, conventional approaches often ad…
Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation
Jingwei Yang, Bohuan Xue, Yi Feng +3
This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity dis…
E3CM: Epipolar-Constrained Cascade Correspondence Matching
Chenbo Zhou, Shuai Su, Qijun Chen +1
Accurate and robust correspondence matching is of utmost importance for various 3D computer vision tasks. However, traditional explicit programming-based methods often struggle to…
Revisiting Perspective Information for Efficient Crowd Counting
Miaojing Shi, Zhaohui Yang, Chao Xu +1
Crowd counting is the task of estimating people numbers in crowd images. Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regres…
Novel View Synthesis for Large-scale Scene using Adversarial Loss
Xiaochuan Yin, Henglai Wei, Penghong lin +2
Novel view synthesis aims to synthesize new images from different viewpoints of given images. Most of previous works focus on generating novel views of certain objects with a fixed…