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 • 10 Dec 2023 • Jianbiao Mei, Yu Yang, Mengmeng Wang, Junyu Zhu, Xiangrui Zhao, Jongwon Ra, Laijian Li, Yong liu
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving.
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
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.