Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include Deep Learning, Video Understanding and Action Recognition.

Research and Interests

• Video Understanding
• Temporal Action Detection

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.}
  }

• Jianbiao Mei, Mengmeng Wang, Yu Yang, Zizhang Li, and Yong Liu. Learning Spatiotemporal Relationships with a Unified Framework for Video Object Segmentation. Applied Intelligence, 54:6138-6153, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Video object segmentation (VOS) has made significant progress with matching-based methods, but most approaches still show two problems. Firstly, they apply a complicated and redundant two-extractor pipeline to use more reference frames for cues, increasing the models’ parameters and complexity. Secondly, most of these methods neglect the spatial relationships (inside each frame) and do not fully model the temporal relationships (among different frames), i.e., they need adequate modeling of spatial-temporal relationships. In this paper, to address the two problems, we propose a unified transformer-based framework for VOS, a compact and unified single-extractor pipeline with strong spatial and temporal interaction ability. Specifically, to slim the common-used two-extractor pipeline while keeping the model’s effectiveness and flexibility, we design a single dynamic feature extractor with an ingenious dynamic input adapter to encode two significant inputs, i.e., reference sets (historical frames with predicted masks) and query frame (current frame), respectively. Moreover, the relationships among different frames and inside every frame are crucial for this task. We introduce a vision transformer to exploit and model both the temporal and spatial relationships simultaneously. By the cascaded design of the proposed dynamic feature extractor, transformer-based relationship module, and target-enhanced segmentation, our model implements a unified and compact pipeline for VOS. Extensive experiments demonstrate the superiority of our model over state-of-the-art methods on both DAVIS and YouTube-VOS datasets. We also explore potential solutions, such as sequence organizers, to improve the model’s efficiency. On DAVIS17 validation, we achieve ∼50% faster inference speed with only a slight 0.2% (J&F) drop in segmentation quality. Codes are available at https://github.com/sallymmx/TransVOS.git.

  @article{mei2024lsr,
  title = {Learning Spatiotemporal Relationships with a Unified Framework for Video Object Segmentation},
  author = {Jianbiao Mei and Mengmeng Wang and Yu Yang and Zizhang Li and Yong Liu},
  year = 2024,
  journal = {Applied Intelligence},
  volume = 54,
  pages = {6138-6153},
  doi = {10.1007/s10489-024-05486-y},
  abstract = {Video object segmentation (VOS) has made significant progress with matching-based methods, but most approaches still show two problems. Firstly, they apply a complicated and redundant two-extractor pipeline to use more reference frames for cues, increasing the models’ parameters and complexity. Secondly, most of these methods neglect the spatial relationships (inside each frame) and do not fully model the temporal relationships (among different frames), i.e., they need adequate modeling of spatial-temporal relationships. In this paper, to address the two problems, we propose a unified transformer-based framework for VOS, a compact and unified single-extractor pipeline with strong spatial and temporal interaction ability. Specifically, to slim the common-used two-extractor pipeline while keeping the model’s effectiveness and flexibility, we design a single dynamic feature extractor with an ingenious dynamic input adapter to encode two significant inputs, i.e., reference sets (historical frames with predicted masks) and query frame (current frame), respectively. Moreover, the relationships among different frames and inside every frame are crucial for this task. We introduce a vision transformer to exploit and model both the temporal and spatial relationships simultaneously. By the cascaded design of the proposed dynamic feature extractor, transformer-based relationship module, and target-enhanced segmentation, our model implements a unified and compact pipeline for VOS. Extensive experiments demonstrate the superiority of our model over state-of-the-art methods on both DAVIS and YouTube-VOS datasets. We also explore potential solutions, such as sequence organizers, to improve the model’s efficiency. On DAVIS17 validation, we achieve ∼50% faster inference speed with only a slight 0.2% (J&F) drop in segmentation quality. Codes are available at https://github.com/sallymmx/TransVOS.git.}
  }

• Jianbiao Mei, Yu Yang, Mengmeng Wang, Zizhang Li, Jongwon Ra, and Yong Liu. LiDAR Video Object Segmentation with Dynamic Kernel Refinement. Pattern Recognition Letters, 178:21-27, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  In this paper, we formalize memory- and tracking-based methods to perform the LiDAR-based Video Object Segmentation (VOS) task, which segments points of the specific 3D target (given in the first frame) in a LiDAR sequence. LiDAR-based VOS can directly provide target-aware geometric information for practical application scenarios like behavior analysis and anticipating danger. We first construct a LiDAR-based VOS dataset named KITTI-VOS based on SemanticKITTI, which acts as a testbed and facilitates comprehensive evaluations of algorithm performance. Next, we provide two types of baselines, i.e., memory-based and tracking-based baselines, to explore this task. Specifically, the first memory-based pipeline is built on a space–time memory network equipped with the non-local spatiotemporal attention-based memory bank. We further design a more potent variant to introduce the locality into the spatiotemporal attention module by local self-attention and cross-attention modules. For the second tracking-based baseline, we modify two representative 3D object tracking methods to adapt to LiDAR-based VOS tasks. Finally, we propose a refine module that takes mask priors and generates object-aware kernels, which could boost all the baselines’ performance. We evaluate the proposed methods on the dataset and demonstrate their effectiveness.

  @article{mei2024lvo,
  title = {LiDAR Video Object Segmentation with Dynamic Kernel Refinement},
  author = {Jianbiao Mei and Yu Yang and Mengmeng Wang and Zizhang Li and Jongwon Ra and Yong Liu},
  year = 2024,
  journal = {Pattern Recognition Letters},
  volume = 178,
  pages = {21-27},
  doi = {10.1016/j.patrec.2023.12.013},
  abstract = {In this paper, we formalize memory- and tracking-based methods to perform the LiDAR-based Video Object Segmentation (VOS) task, which segments points of the specific 3D target (given in the first frame) in a LiDAR sequence. LiDAR-based VOS can directly provide target-aware geometric information for practical application scenarios like behavior analysis and anticipating danger. We first construct a LiDAR-based VOS dataset named KITTI-VOS based on SemanticKITTI, which acts as a testbed and facilitates comprehensive evaluations of algorithm performance. Next, we provide two types of baselines, i.e., memory-based and tracking-based baselines, to explore this task. Specifically, the first memory-based pipeline is built on a space–time memory network equipped with the non-local spatiotemporal attention-based memory bank. We further design a more potent variant to introduce the locality into the spatiotemporal attention module by local self-attention and cross-attention modules. For the second tracking-based baseline, we modify two representative 3D object tracking methods to adapt to LiDAR-based VOS tasks. Finally, we propose a refine module that takes mask priors and generates object-aware kernels, which could boost all the baselines’ performance. We evaluate the proposed methods on the dataset and demonstrate their effectiveness.}
  }

• Jongwon Ra, Mengmeng Wang, Jianbiao Mei, Shanqi Liu, Yu Yang, and Yong Liu. Exploit Spatiotemporal Contextual Information for 3D Single Object Tracking via Memory Networks. In 11th International Conference on 3D Vision (3DV), pages 842-851, 2024.
  [BibTeX] [Abstract] [DOI]

  The point cloud-based 3D single object tracking plays an indispensable role in autonomous driving. However, the application of 3D object tracking in the real world is still challenging due to the inherent sparsity and self-occlusion of point cloud data. Therefore, it is necessary to exploit as much useful information from limited data as we can. Since 3D object tracking is a video-level task, the appearance of objects changes gradually over time, and there is rich spatiotemporal contextual information among historical frames. However, existing methods do not fully utilize this information. To address this, we propose a new method called SCTrack, which utilizes a memory-based paradigm to exploit spatiotemporal contextual information. SCTrack incorporates both long-term and short-term memory banks to store the spatiotemporal features of targets from historical frames. By doing so, the tracker can benefit from the entire video sequence and make more informed predictions. Additionally, SCTrack extracts the mask prior to augmenting the target representation, improving the target-background discriminability. Extensive experiments on KITTI, nuScenes, and Waymo Open datasets verify the effectiveness of our proposed method.

  @inproceedings{Ra2024esc,
  title = {Exploit Spatiotemporal Contextual Information for 3D Single Object Tracking via Memory Networks},
  author = {Jongwon Ra and Mengmeng Wang and Jianbiao Mei and Shanqi Liu and Yu Yang and Yong Liu},
  year = 2024,
  booktitle = {11th International Conference on 3D Vision (3DV)},
  pages = {842-851},
  doi = {10.1109/3DV62453.2024.00050},
  abstract = {The point cloud-based 3D single object tracking plays an indispensable role in autonomous driving. However, the application of 3D object tracking in the real world is still challenging due to the inherent sparsity and self-occlusion of point cloud data. Therefore, it is necessary to exploit as much useful information from limited data as we can. Since 3D object tracking is a video-level task, the appearance of objects changes gradually over time, and there is rich spatiotemporal contextual information among historical frames. However, existing methods do not fully utilize this information. To address this, we propose a new method called SCTrack, which utilizes a memory-based paradigm to exploit spatiotemporal contextual information. SCTrack incorporates both long-term and short-term memory banks to store the spatiotemporal features of targets from historical frames. By doing so, the tracker can benefit from the entire video sequence and make more informed predictions. Additionally, SCTrack extracts the mask prior to augmenting the target representation, improving the target-background discriminability. Extensive experiments on KITTI, nuScenes, and Waymo Open datasets verify the effectiveness of our proposed method.}
  }

• Yu Yang, Mengmeng Wang, Jianbiao Mei, and Yong Liu. Exploiting Semantic-level Affinities with a Mask-Guided Network for Temporal Action Proposal in Videos. Applied Intelligence, 53:15516-15536, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Temporal action proposal (TAP) aims to detect the action instances’ starting and ending times in untrimmed videos, which is fundamental and critical for large-scale video analysis and human action understanding. The main challenge of the temporal action proposal lies in modeling representative temporal relations in long untrimmed videos. Existing state-of-the-art methods achieve temporal modeling by building local-level, proposal-level, or global-level temporal dependencies. Local methods lack a wider receptive field, while proposal and global methods lack the focalization of learning action frames and contain background distractions. In this paper, we propose that learning semantic-level affinities can capture more practical information. Specifically, by modeling semantic associations between frames and action units, action segments (foregrounds) can aggregate supportive cues from other co-occurring actions, and nonaction clips (backgrounds) can learn the discriminations between them and action frames. To this end, we propose a novel framework named the Mask-Guided Network (MGNet) to build semantic-level temporal associations for the TAP task. Specifically, we first propose a Foreground Mask Generation (FMG) module to adaptively generate the foreground mask, representing the locations of the action units throughout the video. Second, we design a Mask-Guided Transformer (MGT) by exploiting the foreground mask to guide the self-attention mechanism to focus on and calculate semantic affinities with the foreground frames. Finally, these two modules are jointly explored in a unified framework. MGNet models the intra-semantic similarities for foregrounds, extracting supportive action cues for boundary refinement; it also builds the inter-semantic distances for backgrounds, providing the semantic gaps to suppress false positives and distractions. Extensive experiments are conducted on two challenging datasets, ActivityNet-1.3 and THUMOS14, and the results demonstrate that our method achieves superior performance.

  @article{yang2023esl,
  title = {Exploiting Semantic-level Affinities with a Mask-Guided Network for Temporal Action Proposal in Videos},
  author = {Yu Yang and Mengmeng Wang and Jianbiao Mei and Yong Liu},
  year = 2023,
  journal = {Applied Intelligence},
  volume = 53,
  pages = {15516-15536},
  doi = {10.1007/s10489-022-04261-1},
  abstract = {Temporal action proposal (TAP) aims to detect the action instances' starting and ending times in untrimmed videos, which is fundamental and critical for large-scale video analysis and human action understanding. The main challenge of the temporal action proposal lies in modeling representative temporal relations in long untrimmed videos. Existing state-of-the-art methods achieve temporal modeling by building local-level, proposal-level, or global-level temporal dependencies. Local methods lack a wider receptive field, while proposal and global methods lack the focalization of learning action frames and contain background distractions. In this paper, we propose that learning semantic-level affinities can capture more practical information. Specifically, by modeling semantic associations between frames and action units, action segments (foregrounds) can aggregate supportive cues from other co-occurring actions, and nonaction clips (backgrounds) can learn the discriminations between them and action frames. To this end, we propose a novel framework named the Mask-Guided Network (MGNet) to build semantic-level temporal associations for the TAP task. Specifically, we first propose a Foreground Mask Generation (FMG) module to adaptively generate the foreground mask, representing the locations of the action units throughout the video. Second, we design a Mask-Guided Transformer (MGT) by exploiting the foreground mask to guide the self-attention mechanism to focus on and calculate semantic affinities with the foreground frames. Finally, these two modules are jointly explored in a unified framework. MGNet models the intra-semantic similarities for foregrounds, extracting supportive action cues for boundary refinement; it also builds the inter-semantic distances for backgrounds, providing the semantic gaps to suppress false positives and distractions. Extensive experiments are conducted on two challenging datasets, ActivityNet-1.3 and THUMOS14, and the results demonstrate that our method achieves superior performance.}
  }

• Jianbiao Mei, Yu Yang, Mengmeng Wang, Zizhang Li, Xiaojun Hou, Jongwon Ra, Laijian Li, and Yong Liu. CenterLPS: Segment Instances by Centers for LiDAR Panoptic Segmentation. In 31st ACM International Conference on Multimedia (MM), pages 1884-1894, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper focuses on LiDAR Panoptic Segmentation (LPS), which has attracted more attention recently due to its broad application prospect for autonomous driving and robotics. The mainstream LPS approaches either adopt a top-down strategy relying on 3D object detectors to discover instances or utilize time-consuming heuristic clustering algorithms to group instances in a bottom-up manner. Inspired by the center representation and kernel-based segmentation, we propose a new detection-free and clustering-free framework called CenterLPS, with the center-based instance encoding and decoding paradigm. Specifically, we propose a sparse center proposal network to generate the sparse 3D instance centers, as well as center feature embedding, which can well encode characteristics of instances. Then a center-aware transformer is applied to collect the context between different center feature embedding and around centers. Moreover, we generate the kernel weights based on the enhanced center feature embedding and initialize dynamic convolutions to decode the final instance masks. Finally, a mask fusion module is devised to unify the semantic and instance predictions and improve the panoptic quality. Extensive experiments on SemanticKITTI and nuScenes demonstrate the effectiveness of our proposed center-based framework CenterLPS.

  @inproceedings{mei2023lps,
  title = {CenterLPS: Segment Instances by Centers for LiDAR Panoptic Segmentation},
  author = {Jianbiao Mei and Yu Yang and Mengmeng Wang and Zizhang Li and Xiaojun Hou and Jongwon Ra and Laijian Li and Yong Liu},
  year = 2023,
  booktitle = {31st ACM International Conference on Multimedia (MM)},
  pages = {1884-1894},
  doi = {10.1145/3581783.3612080},
  abstract = {This paper focuses on LiDAR Panoptic Segmentation (LPS), which has attracted more attention recently due to its broad application prospect for autonomous driving and robotics. The mainstream LPS approaches either adopt a top-down strategy relying on 3D object detectors to discover instances or utilize time-consuming heuristic clustering algorithms to group instances in a bottom-up manner. Inspired by the center representation and kernel-based segmentation, we propose a new detection-free and clustering-free framework called CenterLPS, with the center-based instance encoding and decoding paradigm. Specifically, we propose a sparse center proposal network to generate the sparse 3D instance centers, as well as center feature embedding, which can well encode characteristics of instances. Then a center-aware transformer is applied to collect the context between different center feature embedding and around centers. Moreover, we generate the kernel weights based on the enhanced center feature embedding and initialize dynamic convolutions to decode the final instance masks. Finally, a mask fusion module is devised to unify the semantic and instance predictions and improve the panoptic quality. Extensive experiments on SemanticKITTI and nuScenes demonstrate the effectiveness of our proposed center-based framework CenterLPS.}
  }

• Jianbiao Mei, Yu Yang, Mengmeng Wang, Tianxin Huang, Xuemeng Yang, and Yong Liu. SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7718-7725, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving systems. SSC has achieved rapid progress with the help of semantic context in segmentation. However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration. In this paper, we propose to solve outdoor SSC from the perspective of representation separation and BEV fusion. Specifically, we present the network, named SSC-RS, which uses separate branches with deep supervision to explicitly disentangle the learning procedure of the semantic and geometric representations. And a BEV fusion network equipped with the proposed Adaptive Representation Fusion (ARF) module is presented to aggregate the multi-scale features effectively and efficiently. Due to the low computational burden and powerful representation ability, our model has good generality while running in real-time. Extensive experiments on SemanticKITTI demonstrate our SSC-RS achieves state-of-the-art performance. Code is available at https://github.com/Jieqianyu/SSC-RS.git.

  @inproceedings{mei2023ssc,
  title = {SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion},
  author = {Jianbiao Mei and Yu Yang and Mengmeng Wang and Tianxin Huang and Xuemeng Yang and Yong Liu},
  year = 2023,
  booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {7718-7725},
  doi = {10.1109/IROS55552.2023.10341742},
  abstract = {Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving systems. SSC has achieved rapid progress with the help of semantic context in segmentation. However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration. In this paper, we propose to solve outdoor SSC from the perspective of representation separation and BEV fusion. Specifically, we present the network, named SSC-RS, which uses separate branches with deep supervision to explicitly disentangle the learning procedure of the semantic and geometric representations. And a BEV fusion network equipped with the proposed Adaptive Representation Fusion (ARF) module is presented to aggregate the multi-scale features effectively and efficiently. Due to the low computational burden and powerful representation ability, our model has good generality while running in real-time. Extensive experiments on SemanticKITTI demonstrate our SSC-RS achieves state-of-the-art performance. Code is available at https://github.com/Jieqianyu/SSC-RS.git.}
  }

• Jianbiao Mei, Yu Yang, Mengmeng Wang, Xiaojun Hou, Laijian Li, and Yong Liu. PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7726-7733, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch and improve the performance on large objects, which are always over-segmented by clustering algorithms. Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the “sampling-shifting-grouping” scheme to directly group thing points into instances from the raw point cloud efficiently. More specifically, balanced point sampling is introduced to generate sparse seed points with more uniform point distribution over the distance range. And a shift module, termed bubble shifting, is proposed to shrink the seed points to the clustered centers. Then we utilize the connected component label algorithm to generate instance proposals. Furthermore, an instance aggregation module is devised to integrate potentially fragmented instances, improving the performance of the SIP module on large objects. Extensive experiments show that PANet achieves state-of-the-art performance among published works on the SemanticKITII validation and nuScenes validation for the panoptic segmentation task. Code is available at https://github.com/Jieqianyu/PANet.git.

  @inproceedings{mei2023pan,
  title = {PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation},
  author = {Jianbiao Mei and Yu Yang and Mengmeng Wang and Xiaojun Hou and Laijian Li and Yong Liu},
  year = 2023,
  booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {7726-7733},
  doi = {10.1109/IROS55552.2023.10342468},
  abstract = {Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch and improve the performance on large objects, which are always over-segmented by clustering algorithms. Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the “sampling-shifting-grouping” scheme to directly group thing points into instances from the raw point cloud efficiently. More specifically, balanced point sampling is introduced to generate sparse seed points with more uniform point distribution over the distance range. And a shift module, termed bubble shifting, is proposed to shrink the seed points to the clustered centers. Then we utilize the connected component label algorithm to generate instance proposals. Furthermore, an instance aggregation module is devised to integrate potentially fragmented instances, improving the performance of the SIP module on large objects. Extensive experiments show that PANet achieves state-of-the-art performance among published works on the SemanticKITII validation and nuScenes validation for the panoptic segmentation task. Code is available at https://github.com/Jieqianyu/PANet.git.}
  }