mlsanityData to 14 Oct 2026

PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark

arXiv 2304.06775 ↗First posted 13 Apr 2023cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Point clouds offer comprehensive and precise data regarding the contour and configuration of objects. Employing such geometric and topological 3D information of objects in class incremental learning can aid endless application in 3D-computer vision. Well known 3D-point cloud class incremental learning methods for addressing catastrophic forgetting generally entail the usage of previously encountered data, which can present difficulties in situations where there are restrictions on memory or when there are concerns about the legality of the data. Towards this we pioneer to leverage exemplar free class incremental learning on Point Clouds. In this paper we propose PointCLIMB: An exemplar Free Class Incremental Learning Benchmark. We focus on a pragmatic perspective to consider novel classes for class incremental learning on 3D 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. We demonstrate our results on ModelNet40 dataset.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 13 Apr 2023.

Builds on

16 of 28 references.

References · 16

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  4. 2016Learning without Forgettingcited by 2,496
  5. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  7. 2018End-to-End Incremental Learningcited by 597
  8. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  9. 2020Class-incremental learning: survey and performance evaluation on image classificationcited by 449
  10. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  11. 2021Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learningcited by 131
  12. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
4 more
  1. 2020I3DOL: Incremental 3D Object Learning without Catastrophic Forgettingcited by 20
  2. 2022Few-shot Class-incremental Learning for 3D Point Cloud Objectscited by 13
  3. 2017A Strategy for an Uncompromising Incremental Learnercited by 9
  4. 2019Catastrophic forgetting: still a problem for DNNscited by 7

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selectioncited by 4
  2. 2024A Benchmark Grocery Dataset of Realworld Point Clouds From Single Viewcited by 2
  3. 2025Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Trainingcited by 1