Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Training

• URL: http://arxiv.org/abs/2504.08412v1
• Date: Fri, 11 Apr 2025 10:18:35 GMT
• Title: Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Training
• Authors: Chao Qi, Jianqin Yin, Meng Chen, Yingchun Niu, Yuan Sun,
• Abstract summary: Existing class-incremental learning methods in 3D point clouds rely on exemplars to resist forgetting of models.<n>For exemplar-free incremental learning, the pre-trained model methods have achieved state-of-the-art results in 2D domains.<n>This paper proposes a framework embedded with 3D geometry knowledge for incremental learning in point clouds.
• Score: 12.652126803856065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing class-incremental learning methods in 3D point clouds rely on exemplars (samples of former classes) to resist the catastrophic forgetting of models, and exemplar-free settings will greatly degrade the performance. For exemplar-free incremental learning, the pre-trained model methods have achieved state-of-the-art results in 2D domains. However, these methods cannot be migrated to the 3D domains due to the limited pre-training datasets and insufficient focus on fine-grained geometric details. This paper breaks through these limitations, proposing a basic shape dataset with zero collection cost for model pre-training. It helps a model obtain extensive knowledge of 3D geometries. Based on this, we propose a framework embedded with 3D geometry knowledge for incremental learning in point clouds, compatible with exemplar-free (-based) settings. In the incremental stage, the geometry knowledge is extended to represent objects in point clouds. The class prototype is calculated by regularizing the data representation with the same category and is kept adjusting in the learning process. It helps the model remember the shape features of different categories. Experiments show that our method outperforms other baseline methods by a large margin on various benchmark datasets, considering both exemplar-free (-based) settings.

Related papers

• 3D Point Cloud Generation via Autoregressive Up-sampling [60.05226063558296]
  We introduce a pioneering autoregressive generative model for 3D point cloud generation.<n>Inspired by visual autoregressive modeling, we conceptualize point cloud generation as an autoregressive up-sampling process.<n>PointARU progressively refines 3D point clouds from coarse to fine scales.
  arXiv  Detail & Related papers  (2025-03-11T16:30:45Z)
• Clustering based Point Cloud Representation Learning for 3D Analysis [80.88995099442374]
  We propose a clustering based supervised learning scheme for point cloud analysis. Unlike current de-facto, scene-wise training paradigm, our algorithm conducts within-class clustering on the point embedding space. Our algorithm shows notable improvements on famous point cloud segmentation datasets.
  arXiv  Detail & Related papers  (2023-07-27T03:42:12Z)
• PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark [11.992472563628283]
  We pioneer to leverage exemplar free class incremental learning on Point Clouds. We setup a benchmark for 3D Exemplar free class incremental learning. We investigate performance of various backbones on 3D-Exemplar Free Class Incremental Learning framework.
  arXiv  Detail & Related papers  (2023-04-13T18:47:29Z)
• StarNet: Style-Aware 3D Point Cloud Generation [82.30389817015877]
  StarNet is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network. Our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks.
  arXiv  Detail & Related papers  (2023-03-28T08:21:44Z)
• P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting [94.11915008006483]
  We propose a novel Point-to-Pixel prompting for point cloud analysis. Our method attains 89.3% accuracy on the hardest setting of ScanObjectNN. Our framework also exhibits very competitive performance on ModelNet classification and ShapeNet Part Code.
  arXiv  Detail & Related papers  (2022-08-04T17:59:03Z)
• MetaSets: Meta-Learning on Point Sets for Generalizable Representations [100.5981809166658]
  We study a new problem of 3D Domain Generalization (3DDG) with the goal to generalize the model to other unseen domains of point clouds without access to them in the training process. We propose to tackle this problem via MetaSets, which meta-learns point cloud representations from a group of classification tasks on carefully-designed transformed point sets. We design two benchmarks for Sim-to-Real transfer of 3D point clouds. Experimental results show that MetaSets outperforms existing 3D deep learning methods by large margins.
  arXiv  Detail & Related papers  (2022-04-15T03:24:39Z)
• Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds [54.31515001741987]
  We propose a point discriminative learning method for unsupervised representation learning on 3D point clouds. We achieve this by imposing a novel point discrimination loss on the middle level and global level point features. Our method learns powerful representations and achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-08-04T15:11:48Z)
• Learning Compositional Shape Priors for Few-Shot 3D Reconstruction [36.40776735291117]
  We show that complex encoder-decoder architectures exploit large amounts of per-category data. We propose three ways to learn a class-specific global shape prior, directly from data. Experiments on the popular ShapeNet dataset show that our method outperforms a zero-shot baseline by over 40%.
  arXiv  Detail & Related papers  (2021-06-11T14:55:49Z)
• Learning Generative Models of Textured 3D Meshes from Real-World Images [26.353307246909417]
  We propose a GAN framework for generating textured triangle meshes without relying on such annotations. We show that the performance of our approach is on par with prior work that relies on ground-truth keypoints.
  arXiv  Detail & Related papers  (2021-03-29T14:07:37Z)
• Point Transformer for Shape Classification and Retrieval of 3D and ALS Roof PointClouds [3.3744638598036123]
  This paper proposes a fully attentional model - em Point Transformer, for deriving a rich point cloud representation. The model's shape classification and retrieval performance are evaluated on a large-scale urban dataset - RoofN3D and a standard benchmark dataset ModelNet40. The proposed method outperforms other state-of-the-art models in the RoofN3D dataset, gives competitive results in the ModelNet40 benchmark, and showcases high robustness to various unseen point corruptions.
  arXiv  Detail & Related papers  (2020-11-08T08:11:02Z)
• PointManifold: Using Manifold Learning for Point Cloud Classification [5.705680763604835]
  We propose a point cloud classification method based on graph neural network and manifold learning. This paper uses manifold learning algorithms to embed point cloud features for better considering continuity on the surface. Experiments show that the proposed point cloud classification methods obtain the mean class accuracy (mA) of 90.2% and the overall accuracy (oA) of 93.2%.
  arXiv  Detail & Related papers  (2020-10-14T16:28:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.