mlsanityData to 14 Oct 2026

Self-supervised adversarial masking for 3D point cloud representation learning

arXiv 2307.05325 ↗First posted 11 Jul 2023cs.CVcited by 0

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Abstract (arXiv)

Self-supervised methods have been proven effective for learning deep representations of 3D point cloud data. Although recent methods in this domain often rely on random masking of inputs, the results of this approach can be improved. We introduce PointCAM, a novel adversarial method for learning a masking function for point clouds. Our model utilizes a self-distillation framework with an online tokenizer for 3D point clouds. Compared to previous techniques that optimize patch-level and object-level objectives, we postulate applying an auxiliary network that learns how to select masks instead of choosing them randomly. Our results show that the learned masking function achieves state-of-the-art or competitive performance on various downstream tasks. The source code is available at https://github.com/szacho/pointcam.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 11 Jul 2023.

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37 of 47 references.

References · 37

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25 more
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  1. 2024GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D
  2. 2022Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud Analysis
  3. 2022SeRP: Self-Supervised Representation Learning Using Perturbed Point Clouds
  4. 2022PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition
  5. 2025Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning
  6. 2025Self-Supervised Point Cloud Completion based on Multi-View Augmentations of Single Partial Point Cloud
  7. 2025PointCNN++: Performant Convolution on Native Points
  8. 2023PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds