mlsanityData to 7 Oct 2026

Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation

arXiv 2104.13053 ↗First posted 27 Apr 2021cs.CV cs.MMcited by 1

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Abstract (arXiv)

Self-attention mechanism recently achieves impressive advancement in Natural Language Processing (NLP) and Image Processing domains. And its permutation invariance property makes it ideally suitable for point cloud processing. Inspired by this remarkable success, we propose an end-to-end architecture, dubbed Cross-Level Cross-Scale Cross-Attention Network (CLCSCANet), for point cloud representation learning. First, a point-wise feature pyramid module is introduced to hierarchically extract features from different scales or resolutions. Then a cross-level cross-attention is designed to model long-range inter-level and intra-level dependencies. Finally, we develop a cross-scale cross-attention module to capture interactions between-and-within scales for representation enhancement. Compared with state-of-the-art approaches, our network can obtain competitive performance on challenging 3D object classification, point cloud segmentation tasks via comprehensive experimental evaluation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

22 of 35 references.

References · 22

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  5. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  6. 2018Dual Attention Network for Scene Segmentationcited by 1,001
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  9. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
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  11. 2017A Structured Self-attentive Sentence Embeddingcited by 318
  12. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
10 more
  1. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  2. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  3. 2020Learning multiview 3D point cloud registrationcited by 78
  4. 2018RGCNN: Regularized Graph CNN for Point Cloud Segmentationcited by 70
  5. 2019Spherical Kernel for Efficient Graph Convolution on 3D Point Cloudscited by 57
  6. 2020FPConv: Learning Local Flattening for Point Convolutioncited by 55
  7. 2020Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labelscited by 45
  8. 2019Adaptive Hierarchical Down-Sampling for Point Cloud Classificationcited by 40
  9. 2019Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modelingcited by 25
  10. 2020PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentationcited by 4

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