2 code implementations • 13 Feb 2024 • Jiachen Lu, Renyuan Peng, Xinyue Cai, Hang Xu, Hongyang Li, Feng Wen, Wei zhang, Li Zhang
Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence.
2 code implementations • 31 Jan 2024 • Renyuan Peng, Xinyue Cai, Hang Xu, Jiachen Lu, Feng Wen, Wei zhang, Li Zhang
Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG.
no code implementations • 14 Jan 2024 • Junyu Zhu, Lina Liu, Bofeng Jiang, Feng Wen, Hongbo Zhang, Wanlong Li, Yong liu
In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth.
1 code implementation • 28 Aug 2023 • Junyu Zhu, Lina Liu, Yu Tang, Feng Wen, Wanlong Li, Yong liu
In this paper, we present a novel semi-supervised framework for visual BEV semantic segmentation to boost performance by exploiting unlabeled images during the training.
Autonomous Vehicles Bird's-Eye View Semantic Segmentation +2
1 code implementation • NeurIPS 2023 • Huijie Wang, Tianyu Li, Yang Li, Li Chen, Chonghao Sima, Zhenbo Liu, Bangjun Wang, Peijin Jia, Yuting Wang, Shengyin Jiang, Feng Wen, Hang Xu, Ping Luo, Junchi Yan, Wei zhang, Hongyang Li
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.
1 code implementation • 29 Mar 2023 • Yiheng Li, Canhui Tang, Runzhao Yao, Aixue Ye, Feng Wen, Shaoyi Du
Firstly, we propose to use salient points with prominent local features as nodes to increase patch repeatability, and introduce some uniformly distributed points to complete the point cloud, thus constituting hybrid points.
no code implementations • 20 Jan 2023 • Junyu Zhu, Lina Liu, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods.
no code implementations • ICCV 2023 • Jiachen Lu, Renyuan Peng, Xinyue Cai, Hang Xu, Hongyang Li, Feng Wen, Wei zhang, Li Zhang
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections.
no code implementations • IEEE International Workshop on Intelligent Robots and Systems (IROS) 2021 • Hao Zou, Xuemeng Yang, Tianxin Huang, Chujuan Zhang, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems.
Ranked #6 on 3D Semantic Scene Completion on SemanticKITTI
1 code implementation • 23 Sep 2021 • Xuemeng Yang, Hao Zou, Xin Kong, Tianxin Huang, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #4 on 3D Semantic Scene Completion on SemanticKITTI
no code implementations • 22 Jun 2021 • Lin Li, Xin Kong, Xiangrui Zhao, Wanlong Li, Feng Wen, Hongbo Zhang, Yong liu
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue.
no code implementations • 22 Oct 2020 • Hao Zou, Jinhao Cui, Xin Kong, Chujuan Zhang, Yong liu, Feng Wen, Wanlong Li
A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates.
1 code implementation • 26 Aug 2020 • Xin Kong, Xuemeng Yang, Guangyao Zhai, Xiangrui Zhao, Xianfang Zeng, Mengmeng Wang, Yong liu, Wanlong Li, Feng Wen
First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud.