Biography

I am a M.S. student of Zhejiang Univ, Institute for Control Science and Engineering, majoring in Computer Vision and Artifical Intelligence advised by Prof. Yong Liu. I received Bachelor of Mechanical Engineering from School Of Mechanical Engineering at Zhejiang Univ. in 2019.07. My primary interest lies in responsible usages of Deep Learning to understand the 3D scenes – 3D Computer Vision. Please feel free to reach out via my email if your research interests are in line with me!

Research and Interests

• Deep Learning
• 3D Computer Vision

Publications

• Tianxin Huang, Hao Zou, Jinhao Cui, Jiangning Zhang, Xuemeng Yang, Lin Li, and Yong Liu. Adaptive Recurrent Forward Network for Dense Point Cloud Completion. IEEE Transactions on Multimedia, 25:5903-5915, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  Point cloud completion is an interesting and challenging task in 3D vision, which aims to recover complete shapes from sparse and incomplete point clouds. Existing completion networks often require a vast number of parameters and substantial computational costs to achieve a high performance level, which may limit their practical application. In this work, we propose a novel Adaptive efficient Recurrent Forward Network (ARFNet), which is composed of three parts: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). In an RFE, multiple short global features are extracted from incomplete point clouds, while a dense quantity of completed results are generated in a coarse-to-fine pipeline in the FDC. Finally, we propose the Adamerge module to preserve the details from the original models by merging the generated results with the original incomplete point clouds in the RSP. In addition, we introduce the Sampling Chamfer Distance to better capture the shapes of the models and the balanced expansion constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve state-of-the-art completion performances on dense point clouds with fewer parameters, smaller model sizes, lower memory costs and a faster convergence.

  @article{huang2022arf,
  title = {Adaptive Recurrent Forward Network for Dense Point Cloud Completion},
  author = {Tianxin Huang and Hao Zou and Jinhao Cui and Jiangning Zhang and Xuemeng Yang and Lin Li and Yong Liu},
  year = 2022,
  journal = {IEEE Transactions on Multimedia},
  volume = {25},
  pages = {5903-5915},
  doi = {10.1109/TMM.2022.3200851},
  abstract = {Point cloud completion is an interesting and challenging task in 3D vision, which aims to recover complete shapes from sparse and incomplete point clouds. Existing completion networks often require a vast number of parameters and substantial computational costs to achieve a high performance level, which may limit their practical application. In this work, we propose a novel Adaptive efficient Recurrent Forward Network (ARFNet), which is composed of three parts: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). In an RFE, multiple short global features are extracted from incomplete point clouds, while a dense quantity of completed results are generated in a coarse-to-fine pipeline in the FDC. Finally, we propose the Adamerge module to preserve the details from the original models by merging the generated results with the original incomplete point clouds in the RSP. In addition, we introduce the Sampling Chamfer Distance to better capture the shapes of the models and the balanced expansion constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve state-of-the-art completion performances on dense point clouds with fewer parameters, smaller model sizes, lower memory costs and a faster convergence.}
  }

• Tianxin Huang, Xuemeng Yang, Jiangning Zhang, Jinhao Cui, Hao Zou, Jun Chen and Xiangrui Zhao, and Yong Liu. Learning to Train a Point Cloud Reconstruction Network Without Matching. In European Conference on Computer Vision (ECCV), 2022.
  [BibTeX] [Abstract] [DOI]

  Reconstruction networks for well-ordered data such as 2D images and 1D continuous signals are easy to optimize through element-wised squared errors, while permutation-arbitrary point clouds cannot be constrained directly because their points permutations are not fixed. Though existing works design algorithms to match two point clouds and evaluate shape errors based on matched results, they are limited by pre-defined matching processes. In this work, we propose a novel framework named PCLossNet which learns to train a point cloud reconstruction network without any matching. By training through an adversarial process together with the reconstruction network, PCLossNet can better explore the differences between point clouds and create more precise reconstruction results. Experiments on multiple datasets prove the superiority of our method, where PCLossNet can help networks achieve much lower reconstruction errors and extract more representative features, with about 4 times faster training efficiency than the commonly-used EMD loss. Our codes can be found in https://github.com/Tianxinhuang/PCLossNet.

  @inproceedings{huang2022ltt,
  title = {Learning to Train a Point Cloud Reconstruction Network Without Matching},
  author = {Tianxin Huang and Xuemeng Yang and Jiangning Zhang and Jinhao Cui and Hao Zou and Jun Chen and Xiangrui Zhao and Yong Liu},
  year = 2022,
  booktitle = {European Conference on Computer Vision (ECCV)},
  doi = {10.1007/978-3-031-19769-7_11},
  abstract = {Reconstruction networks for well-ordered data such as 2D images and 1D continuous signals are easy to optimize through element-wised squared errors, while permutation-arbitrary point clouds cannot be constrained directly because their points permutations are not fixed. Though existing works design algorithms to match two point clouds and evaluate shape errors based on matched results, they are limited by pre-defined matching processes. In this work, we propose a novel framework named PCLossNet which learns to train a point cloud reconstruction network without any matching. By training through an adversarial process together with the reconstruction network, PCLossNet can better explore the differences between point clouds and create more precise reconstruction results. Experiments on multiple datasets prove the superiority of our method, where PCLossNet can help networks achieve much lower reconstruction errors and extract more representative features, with about 4 times faster training efficiency than the commonly-used EMD loss. Our codes can be found in https://github.com/Tianxinhuang/PCLossNet.}
  }

• Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang and Yi Yuan, Yifan Xu, and Yong Liu. RFNet: Recurrent Forward Network for Dense Point Cloud Completion. In 2021 International Conference on Computer Vision, pages 12488-12497, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In this paper, we propose a novel Recurrent Forward Network (RFNet), which is composed of three modules: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline. The RSP introduces details from the original incomplete models to refine the completion results. Besides, we propose a Sampling Chamfer Distance to better capture the shapes of models and a new Balanced Expansion Constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve the state-of-the-art with lower memory cost and faster convergence.

  @inproceedings{huang2021rfnetrf,
  title = {RFNet: Recurrent Forward Network for Dense Point Cloud Completion},
  author = {Tianxin Huang and Hao Zou and Jinhao Cui and Xuemeng Yang and Mengmeng Wang and Xiangrui Zhao and Jiangning Zhang and Yi Yuan and Yifan Xu and Yong Liu},
  year = 2021,
  booktitle = {2021 International Conference on Computer Vision},
  pages = {12488-12497},
  doi = {https://doi.org/10.1109/ICCV48922.2021.01228},
  abstract = {Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In this paper, we propose a novel Recurrent Forward Network (RFNet), which is composed of three modules: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline. The RSP introduces details from the original incomplete models to refine the completion results. Besides, we propose a Sampling Chamfer Distance to better capture the shapes of models and a new Balanced Expansion Constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve the state-of-the-art with lower memory cost and faster convergence.}
  }

• Shanqi Liu, licheng Wen, Jinhao Cui, Xuemeng Yang, Junjie Cao, and Yong Liu. Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4777-4784, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  Multi-agent path finding in formation has manypotential real-world applications like mobile warehouse robotics. However, previous multi-agent path finding (MAPF) methods hardly take formation into consideration. Furthermore, they are usually centralized planners and require the whole state of the environment. Other decentralized partially observable approaches to MAPF are reinforcement learning (RL) methods. However, these RL methods encounter difficulties when learning path finding and formation problems at the same time. In this paper, we propose a novel decentralized partially observable RL algorithm that uses a hierarchical structure to decompose the multi-objective task into unrelated ones. It also calculates a theoretical weight that makes each tasks reward has equal influence on the final RL value function. Additionally, we introduce a communication method that helps agents cooperate with each other. Experiments in simulation show that our method outperforms other end-toend RL methods and our method can naturally scale to large world sizes where centralized planner struggles. We also deploy and validate our method in a real-world scenario.

  @inproceedings{liu2021movingfi,
  title = {Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together},
  author = {Shanqi Liu and licheng Wen and Jinhao Cui and Xuemeng Yang and Junjie Cao and Yong Liu},
  year = 2021,
  booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  pages = {4777-4784},
  doi = {https://doi.org/10.1109/IROS51168.2021.9636224},
  abstract = {Multi-agent path finding in formation has manypotential real-world applications like mobile warehouse robotics. However, previous multi-agent path finding (MAPF) methods hardly take formation into consideration. Furthermore, they are usually centralized planners and require the whole state of the environment. Other decentralized partially observable approaches to MAPF are reinforcement learning (RL) methods. However, these RL methods encounter difficulties when learning path finding and formation problems at the same time. In this paper, we propose a novel decentralized partially observable RL algorithm that uses a hierarchical structure to decompose the multi-objective task into unrelated ones. It also calculates a theoretical weight that makes each tasks reward has equal influence on the final RL value function. Additionally, we introduce a communication method that helps agents cooperate with each other. Experiments in simulation show that our method outperforms other end-toend RL methods and our method can naturally scale to large world sizes where centralized planner struggles. We also deploy and validate our method in a real-world scenario.}
  }

• Jinhao Cui, Hao Zou, Xin Kong, Xuemeng Yang, Xiangrui Zhao, Yong Liu, Wanlong Li, Feng Wen, and Hongbo Zhang. PocoNet: SLAM-oriented 3D LiDAR Point Cloud Online Compression Network. In 2021 IEEE International Conference on Robotics and Automation, pages 1868-1874, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  In this paper, we present PocoNet: Point cloud Online COmpression NETwork to address the task of SLAM- oriented compression. The aim of this task is to select a compact subset of points with high priority to maintain localization accuracy. The key insight is that points with high priority have similar geometric features in SLAM scenarios. Hence, we tackle this task as point cloud segmentation to capture complex geometric information. We calculate observation counts by matching between maps and point clouds and divide them into different priority levels. Trained by labels annotated with such observation counts, the proposed network could evaluate the point-wise priority. Experiments are conducted by integrating our compression module into an existing SLAM system to evaluate compression ratios and localization performances. Ex- perimental results on two different datasets verify the feasibility and generalization of our approach.

  @inproceedings{cui2021poconetso,
  title = {PocoNet: SLAM-oriented 3D LiDAR Point Cloud Online Compression Network},
  author = {Jinhao Cui and Hao Zou and Xin Kong and Xuemeng Yang and Xiangrui Zhao and Yong Liu and Wanlong Li and Feng Wen and Hongbo Zhang},
  year = 2021,
  booktitle = {2021 IEEE International Conference on Robotics and Automation},
  pages = {1868-1874},
  doi = {https://doi.org/10.1109/ICRA48506.2021.9561309},
  abstract = {In this paper, we present PocoNet: Point cloud Online COmpression NETwork to address the task of SLAM- oriented compression. The aim of this task is to select a compact subset of points with high priority to maintain localization accuracy. The key insight is that points with high priority have similar geometric features in SLAM scenarios. Hence, we tackle this task as point cloud segmentation to capture complex geometric information. We calculate observation counts by matching between maps and point clouds and divide them into different priority levels. Trained by labels annotated with such observation counts, the proposed network could evaluate the point-wise priority. Experiments are conducted by integrating our compression module into an existing SLAM system to evaluate compression ratios and localization performances. Ex- perimental results on two different datasets verify the feasibility and generalization of our approach.}
  }

• Hao Zou, Jinhao Cui, Xin Kong, Chujuan Zhang, Yong Liu, Feng Wen, and Wanlong Li. F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 8133–8139, 2020.
  [BibTeX] [Abstract] [DOI] [arXiv] [PDF]

  This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. Instead of solely relying on 3D proposals, firstly, our method leverages the Siamese network applied on RGB images to produce 2D region proposals which are then extruded into 3D viewing frustums. Besides, we perform an on-line accuracy validation on the 3D frustum to generate refined point cloud searching space, which can be embedded directly into the existing 3D tracking backbone. For efficiency, our approach gains better performance with fewer candidates by reducing search space. In addition, benefited from introducing the online accuracy validation, for occasional cases with strong occlusions or very sparse points, our approach can still achieve high precision, even when the 2D Siamese tracker loses the target. This approach allows us to set a new state-of-the-art in 3D single object tracking by a significant margin on a sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single object tracking show that our framework boosts 2D tracking performance as well.

  @inproceedings{zou2020fsiameseta,
  title = {F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking},
  author = {Hao Zou and Jinhao Cui and Xin Kong and Chujuan Zhang and Yong Liu and Feng Wen and Wanlong Li},
  year = 2020,
  booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {8133--8139},
  doi = {ttps://doi.org/10.1109/IROS45743.2020.9341120},
  abstract = {This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. Instead of solely relying on 3D proposals, firstly, our method leverages the Siamese network applied on RGB images to produce 2D region proposals which are then extruded into 3D viewing frustums. Besides, we perform an on-line accuracy validation on the 3D frustum to generate refined point cloud searching space, which can be embedded directly into the existing 3D tracking backbone. For efficiency, our approach gains better performance with fewer candidates by reducing search space. In addition, benefited from introducing the online accuracy validation, for occasional cases with strong occlusions or very sparse points, our approach can still achieve high precision, even when the 2D Siamese tracker loses the target. This approach allows us to set a new state-of-the-art in 3D single object tracking by a significant margin on a sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single object tracking show that our framework boosts 2D tracking performance as well.},
  arxiv = {https://arxiv.org/pdf/2010.11510.pdf}
  }