most citedDual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving

5 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20235 cited

Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving

Xinyu Zhang, Li Wang, Jian Chen +11

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D r…

cs.CV20231 cited

Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS

Li Wang, Xinyu Zhang, Fachuan Zhao +6

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overrel…

cs.CV20231 cited

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…

cs.CV20232 cited

MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings

Lei Yang, Jiaxin Yu, Xinyu Zhang +6

Although the majority of recent autonomous driving systems concentrate on developing perception methods based on ego-vehicle sensors, there is an overlooked alternative approach th…

cs.CV2023

Informative Data Selection with Uncertainty for Multi-modal Object Detection

Xinyu Zhang, Zhiwei Li, Zhenhong Zou +5

Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurat…

cs.CV2022

UFO: Unified Feature Optimization

Teng Xi, Yifan Sun, Deli Yu +13

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collecti…