mlsanityData to 14 Oct 2026

PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis

arXiv 2302.05201 ↗First posted 10 Feb 2023cs.CVcited by 7

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Abstract (arXiv)

With recent success of deep learning in 2D visual recognition, deep learning-based 3D point cloud analysis has received increasing attention from the community, especially due to the rapid development of autonomous driving technologies. However, most existing methods directly learn point features in the spatial domain, leaving the local structures in the spectral domain poorly investigated. In this paper, we introduce a new method, PointWavelet, to explore local graphs in the spectral domain via a learnable graph wavelet transform. Specifically, we first introduce the graph wavelet transform to form multi-scale spectral graph convolution to learn effective local structural representations. To avoid the time-consuming spectral decomposition, we then devise a learnable graph wavelet transform, which significantly accelerates the overall training process. Extensive experiments on four popular point cloud datasets, ModelNet40, ScanObjectNN, ShapeNet-Part, and S3DIS, demonstrate the effectiveness of the proposed method on point cloud classification and segmentation.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 64 of its 108 references that are in the map. First posted on arXiv 10 Feb 2023.

Builds on

64 of 108 references.

References · 64

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38 more
  1. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
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  7. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
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  14. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
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  18. 2020PointGroup: Dual-Set Point Grouping for 3D Instance Segmentationcited by 321
  19. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  20. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 239
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50 most cited of 64 All 64 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 1; 2026: 1.

7 papers.

  1. 2024Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learningcited by 43
  2. 2024Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Modelscited by 7
  3. 2024PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecturecited by 3
  4. 2024PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysiscited by 2
  5. 2026STS-Mixer: Spatio-Temporal-Spectral Mixer for 4D Point Cloud Video Understandingcited by 1
  6. 2024Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Techniquecited by 0
  7. 2025SM3D: Mitigating Spectral Bias and Semantic Dilution in Point Cloud State Space Modelscited by 0