title: Point Transformer V3: Simpler, Faster, Stronger
summary: This paper is not motivated to seek innovation within the attention
  mechanism. Instead, it focuses on overcoming the existing trade-offs between
  accuracy and efficiency within the context of point cloud processing,
  leveraging the power of scale. Drawing inspiration from recent advances in 3D
  large-scale representation learning, we recognize that model performance is
  more influenced by scale than by intricate design. Therefore, we present Point
  Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the
  accuracy of certain mechanisms that are minor to the overall performance after
  scaling, such as replacing the precise neighbor search by KNN with an efficient
  serialized neighbor mapping of point clouds organized with specific patterns.
  This principle enables significant scaling, expanding the receptive field from
  16 to 1024 points while remaining efficient (a 3x increase in processing speed
  and a 10x improvement in memory efficiency compared with its predecessor,
  PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that
  span both indoor and outdoor scenarios. Further enhanced with multi-dataset
  joint training, PTv3 pushes these results to a higher level.
paper_authors._id: 657fd7ca156fdf03010c5675
paper_authors.name: Xiaoyang Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-18T10:25:14.387Z
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paper_authors.fullname: Xiaoyang Wu
paper_authors.type: user
paper_authors.user: Gofinge
paper_authors._id: 657fd7ca156fdf03010c5676
paper_authors.name: Li Jiang
paper_authors._id: 657fd7ca156fdf03010c5677
paper_authors.name: Peng-Shuai Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-18T10:31:06.386Z
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paper_authors.fullname: Peng-Shuai Wang
paper_authors.type: user
paper_authors.user: wangps
paper_authors._id: 657fd7ca156fdf03010c5678
paper_authors.name: Zhijian Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Zhijian Liu
paper_authors.type: user
paper_authors.user: zhijianliu
paper_authors._id: 657fd7ca156fdf03010c5679
paper_authors.name: Xihui Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-03T18:29:53.041Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_authors._id: 657fd7ca156fdf03010c567a
paper_authors.name: Yu Qiao
paper_authors._id: 657fd7ca156fdf03010c567b
paper_authors.name: Wanli Ouyang
paper_authors._id: 657fd7ca156fdf03010c567c
paper_authors.name: Tong He
paper_authors._id: 657fd7ca156fdf03010c567d
paper_authors.name: Hengshuang Zhao
paper_title: Point Transformer V3: Simpler, Faster, Stronger
paper_summary: This paper is not motivated to seek innovation within the attention
  mechanism. Instead, it focuses on overcoming the existing trade-offs between
  accuracy and efficiency within the context of point cloud processing,
  leveraging the power of scale. Drawing inspiration from recent advances in 3D
  large-scale representation learning, we recognize that model performance is
  more influenced by scale than by intricate design. Therefore, we present Point
  Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the
  accuracy of certain mechanisms that are minor to the overall performance after
  scaling, such as replacing the precise neighbor search by KNN with an efficient
  serialized neighbor mapping of point clouds organized with specific patterns.
  This principle enables significant scaling, expanding the receptive field from
  16 to 1024 points while remaining efficient (a 3x increase in processing speed
  and a 10x improvement in memory efficiency compared with its predecessor,
  PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that
  span both indoor and outdoor scenarios. Further enhanced with multi-dataset
  joint training, PTv3 pushes these results to a higher level.
paper_ai_keywords: Point Transformer V3 (PTv3) neighbor search KNN serialized neighbor mapping receptive field processing speed memory efficiency multi-dataset joint training state-of-the-art results