Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University after getting my B.S. degree in Automation from Wuhan University in 2021. My major research interest is Depth Estimation.

Research and Interests

• Depth Estimation
• BEV perception
• Semi-supervised Learning

Publications

• Jianbiao Mei, Yu Yang, Mengmeng Wang, Junyu Zhu, Jongwon Ra, Yukai Ma, Laijian Li, and Yong Liu. Camera-Based 3D Semantic Scene Completion With Sparse Guidance Network. IEEE Transactions on Image Processing, 33:5468-5481, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  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. Recently, many studies have turned to camera-based SSC solutions due to the richer visual cues and cost-effectiveness of cameras. However, existing methods usually rely on sophisticated and heavy 3D models to process the lifted 3D features directly, which are not discriminative enough for clear segmentation boundaries. In this paper, we adopt the dense-sparse-dense design and propose a one-stage camera-based SSC framework, termed SGN, to propagate semantics from the semantic-aware seed voxels to the whole scene based on spatial geometry cues. Firstly, to exploit depth-aware context and dynamically select sparse seed voxels, we redesign the sparse voxel proposal network to process points generated by depth prediction directly with the coarse-to-fine paradigm. Furthermore, by designing hybrid guidance (sparse semantic and geometry guidance) and effective voxel aggregation for spatial geometry cues, we enhance the feature separation between different categories and expedite the convergence of semantic propagation. Finally, we devise the multi-scale semantic propagation module for flexible receptive fields while reducing the computation resources. Extensive experimental results on the SemanticKITTI and SSCBench-KITTI-360 datasets demonstrate the superiority of our SGN over existing state-of-the-art methods. And even our lightweight version SGN-L achieves notable scores of 14.80% mIoU and 45.45% IoU on SeamnticKITTI validation with only 12.5 M parameters and 7.16 G training memory. Code is available at https://github.com/Jieqianyu/SGN.

  @article{mei2024cbs,
  title = {Camera-Based 3D Semantic Scene Completion With Sparse Guidance Network},
  author = {Jianbiao Mei and Yu Yang and Mengmeng Wang and Junyu Zhu and Jongwon Ra and Yukai Ma and Laijian Li and Yong Liu},
  year = 2024,
  journal = {IEEE Transactions on Image Processing},
  volume = 33,
  pages = {5468-5481},
  doi = {10.1109/TIP.2024.3461989},
  abstract = {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. Recently, many studies have turned to camera-based SSC solutions due to the richer visual cues and cost-effectiveness of cameras. However, existing methods usually rely on sophisticated and heavy 3D models to process the lifted 3D features directly, which are not discriminative enough for clear segmentation boundaries. In this paper, we adopt the dense-sparse-dense design and propose a one-stage camera-based SSC framework, termed SGN, to propagate semantics from the semantic-aware seed voxels to the whole scene based on spatial geometry cues. Firstly, to exploit depth-aware context and dynamically select sparse seed voxels, we redesign the sparse voxel proposal network to process points generated by depth prediction directly with the coarse-to-fine paradigm. Furthermore, by designing hybrid guidance (sparse semantic and geometry guidance) and effective voxel aggregation for spatial geometry cues, we enhance the feature separation between different categories and expedite the convergence of semantic propagation. Finally, we devise the multi-scale semantic propagation module for flexible receptive fields while reducing the computation resources. Extensive experimental results on the SemanticKITTI and SSCBench-KITTI-360 datasets demonstrate the superiority of our SGN over existing state-of-the-art methods. And even our lightweight version SGN-L achieves notable scores of 14.80% mIoU and 45.45% IoU on SeamnticKITTI validation with only 12.5 M parameters and 7.16 G training memory. Code is available at https://github.com/Jieqianyu/SGN.}
  }

• Linpeng Peng, Rongyao Cai, Jingyang Xiang, Junyu Zhu, Weiwei Liu, Wang Gao, and Yong Liu. LiteGrasp: A Light Robotic Grasp Detection via Semi-Supervised Knowledge Distillation. IEEE Robotics and Automation Letters, 9:7995-8002, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Grasping detection from single images in robotic applications poses a significant challenge. While contemporary deep learning techniques excel, their success often hinges on large annotated datasets and intricate network architectures. In this letter, we present LiteGrasp, a novel semi-supervised lightweight framework purpose-built for grasp detection, eliminating the necessity for exhaustive supervision and intricate networks. Our approach uses a limited amount of labeled data via a knowledge distillation method, introducing HRGrasp-Net, a model with high efficiency for extracting features and largely based on HRNet. We incorporate pseudo-label filtering within a mutual learning model set within a teacher-student paradigm. This enhances the transference of data from images with labels to those without. Additionally, we introduce the streamlined Lite HRGrasp-Net, acting as the student network which gains further distillation knowledge using a multi-level fusion cascade originating from HRGrasp-Net. Impressively, LiteGrasp thrives with just a fraction (4.3%) of HRGrasp-Net’s original model size, and with limited labeled data relative to total data (25% ratio) across all benchmarks, regularly outperforming solely supervised and semi-supervised models. Taking just 6 ms for execution, LiteGrasp showcases exceptional accuracy (99.99% and 97.21% on Cornell and Jacquard data sets respectively), as well as an impressive 95.3% rate of success in grasping when deployed using a 6DoF UR5e robotic arm. These highlights underscore the effectiveness and efficiency of LiteGrasp for grasp detection, even under resource-limited conditions.

  @article{peng2024lal,
  title = {LiteGrasp: A Light Robotic Grasp Detection via Semi-Supervised Knowledge Distillation},
  author = {Linpeng Peng and Rongyao Cai and Jingyang Xiang and Junyu Zhu and Weiwei Liu and Wang Gao and Yong Liu},
  year = 2024,
  journal = {IEEE Robotics and Automation Letters},
  volume = 9,
  pages = {7995-8002},
  doi = {10.1109/LRA.2024.3436336},
  abstract = {Grasping detection from single images in robotic applications poses a significant challenge. While contemporary deep learning techniques excel, their success often hinges on large annotated datasets and intricate network architectures. In this letter, we present LiteGrasp, a novel semi-supervised lightweight framework purpose-built for grasp detection, eliminating the necessity for exhaustive supervision and intricate networks. Our approach uses a limited amount of labeled data via a knowledge distillation method, introducing HRGrasp-Net, a model with high efficiency for extracting features and largely based on HRNet. We incorporate pseudo-label filtering within a mutual learning model set within a teacher-student paradigm. This enhances the transference of data from images with labels to those without. Additionally, we introduce the streamlined Lite HRGrasp-Net, acting as the student network which gains further distillation knowledge using a multi-level fusion cascade originating from HRGrasp-Net. Impressively, LiteGrasp thrives with just a fraction (4.3%) of HRGrasp-Net's original model size, and with limited labeled data relative to total data (25% ratio) across all benchmarks, regularly outperforming solely supervised and semi-supervised models. Taking just 6 ms for execution, LiteGrasp showcases exceptional accuracy (99.99% and 97.21% on Cornell and Jacquard data sets respectively), as well as an impressive 95.3% rate of success in grasping when deployed using a 6DoF UR5e robotic arm. These highlights underscore the effectiveness and efficiency of LiteGrasp for grasp detection, even under resource-limited conditions.}
  }

• Junyu Zhu, Lina Liu, Yu Tang, Feng Wen, wanlong li, and Yong Liu. Semi-Supervised Learning for Visual Bird’s Eye View Semantic Segmentation. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 9079-9085, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Visual bird’s eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from front-view (FV) images, including static elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians). However, the high cost of annotation procedures of full-supervised methods limits the capability of the visual BEV semantic segmentation, which usually needs HD maps, 3D object bounding boxes, and camera extrinsic matrixes. 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. A consistency loss that makes full use of unlabeled data is then proposed to constrain the model on not only semantic prediction but also the BEV feature. Furthermore, we propose a novel and effective data augmentation method named conjoint rotation which reasonably augments the dataset while maintaining the geometric relationship between the FV images and the BEV semantic segmentation. Extensive experiments on the nuScenes dataset show that our semi-supervised framework can effectively improve prediction accuracy. To the best of our knowledge, this is the first work that explores improving visual BEV semantic segmentation performance using unlabeled data. The code is available at https://github.com/Junyu-Z/Semi-BEVseg.

  @inproceedings{zhu2024ssl,
  title = {Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation},
  author = {Junyu Zhu and Lina Liu and Yu Tang and Feng Wen and wanlong li and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {9079-9085},
  doi = {10.1109/ICRA57147.2024.10611420},
  abstract = {Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from front-view (FV) images, including static elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians). However, the high cost of annotation procedures of full-supervised methods limits the capability of the visual BEV semantic segmentation, which usually needs HD maps, 3D object bounding boxes, and camera extrinsic matrixes. 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. A consistency loss that makes full use of unlabeled data is then proposed to constrain the model on not only semantic prediction but also the BEV feature. Furthermore, we propose a novel and effective data augmentation method named conjoint rotation which reasonably augments the dataset while maintaining the geometric relationship between the FV images and the BEV semantic segmentation. Extensive experiments on the nuScenes dataset show that our semi-supervised framework can effectively improve prediction accuracy. To the best of our knowledge, this is the first work that explores improving visual BEV semantic segmentation performance using unlabeled data. The code is available at https://github.com/Junyu-Z/Semi-BEVseg.}
  }

• Junyu Zhu, Lina Liu, Bofeng Jiang, Feng Wen, Hongbo Zhang, Wanlong li, and Yong Liu. Self-Supervised Event-Based Monocular Depth Estimation Using Cross-Modal Consistency. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7704-7710, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  An event camera is a novel vision sensor that can capture per-pixel brightness changes and output a stream of asynchronous “events”. It has advantages over conventional cameras in those scenes with high-speed motions and challenging lighting conditions because of the high temporal resolution, high dynamic range, low bandwidth, low power consumption, and no motion blur. Therefore, several supervised monocular depth estimation from events is proposed to address scenes difficult for conventional cameras. However, depth annotation is costly and time-consuming. In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth. EMoDepth constrains the training process using the cross-modal consistency from intensity frames that are aligned with events in the pixel coordinate. Moreover, in inference, only events are used for monocular depth prediction. Additionally, we design a multi-scale skip-connection architecture to effectively fuse features for depth estimation while maintaining high inference speed. Experiments on MVSEC and DSEC datasets demonstrate that our contributions are effective and that the accuracy can outperform existing supervised event-based and unsupervised frame-based methods.

  @inproceedings{zhu2023sse,
  title = {Self-Supervised Event-Based Monocular Depth Estimation Using Cross-Modal Consistency},
  author = {Junyu Zhu and Lina Liu and Bofeng Jiang and Feng Wen and Hongbo Zhang and Wanlong li and Yong Liu},
  year = 2023,
  booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {7704-7710},
  doi = {10.1109/IROS55552.2023.10342434},
  abstract = {An event camera is a novel vision sensor that can capture per-pixel brightness changes and output a stream of asynchronous “events”. It has advantages over conventional cameras in those scenes with high-speed motions and challenging lighting conditions because of the high temporal resolution, high dynamic range, low bandwidth, low power consumption, and no motion blur. Therefore, several supervised monocular depth estimation from events is proposed to address scenes difficult for conventional cameras. However, depth annotation is costly and time-consuming. In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth. EMoDepth constrains the training process using the cross-modal consistency from intensity frames that are aligned with events in the pixel coordinate. Moreover, in inference, only events are used for monocular depth prediction. Additionally, we design a multi-scale skip-connection architecture to effectively fuse features for depth estimation while maintaining high inference speed. Experiments on MVSEC and DSEC datasets demonstrate that our contributions are effective and that the accuracy can outperform existing supervised event-based and unsupervised frame-based methods.}
  }

• Junyu Zhu, Lina Liu, Yong Liu, Wanlong Li, Feng Wen, and Hongbo Zhang. FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 4924-4930, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  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. To further improve the performance, recent works mainly focus on designing more complex network structures and exploiting extra supervised information, e.g., semantic segmentation. These methods optimize the models by exploiting the reconstructed relationship between the target and reference images in varying degrees. However, previous methods prove that this image reconstruction optimization is prone to get trapped in local minima. In this paper, our core idea is to guide the optimization with prior knowledge from pretrained Flow-Net. And we show that the bottleneck of unsupervised monocular depth estimation can be broken with our simple but effective framework named FG-Depth. In particular, we propose (i) a flow distillation loss to replace the typical photometric loss that limits the capacity of the model and (ii) a prior flow based mask to remove invalid pixels that bring the noise in training loss. Extensive experiments demonstrate the effectiveness of each component, and our approach achieves state-of-the-art results on both KITTI and NYU-Depth-v2 datasets.

  @inproceedings{zhu2023fgd,
  title = {FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation},
  author = {Junyu Zhu and Lina Liu and Yong Liu and Wanlong Li and Feng Wen and Hongbo Zhang},
  year = 2023,
  booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {4924-4930},
  doi = {10.1109/ICRA48891.2023.10160534},
  abstract = {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. To further improve the performance, recent works mainly focus on designing more complex network structures and exploiting extra supervised information, e.g., semantic segmentation. These methods optimize the models by exploiting the reconstructed relationship between the target and reference images in varying degrees. However, previous methods prove that this image reconstruction optimization is prone to get trapped in local minima. In this paper, our core idea is to guide the optimization with prior knowledge from pretrained Flow-Net. And we show that the bottleneck of unsupervised monocular depth estimation can be broken with our simple but effective framework named FG-Depth. In particular, we propose (i) a flow distillation loss to replace the typical photometric loss that limits the capacity of the model and (ii) a prior flow based mask to remove invalid pixels that bring the noise in training loss. Extensive experiments demonstrate the effectiveness of each component, and our approach achieves state-of-the-art results on both KITTI and NYU-Depth-v2 datasets.}
  }