ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation

• URL: http://arxiv.org/abs/2504.01648v2
• Date: Sat, 12 Apr 2025 08:17:42 GMT
• Title: ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation
• Authors: Haosheng Li, Yuecong Xu, Junjie Chen, Kemi Ding,
• Abstract summary: offline-trained segmentation models may lead to catastrophic forgetting of previously seen classes.<n>ProtoGuard and PROPEL are proposed to address the problem of catastrophic forgetting.<n>Our approach achieves remarkable results on both the S3DIS and ScanNet datasets.
• Score: 9.578322021478426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D point cloud semantic segmentation technology has been widely used. However, in real-world scenarios, the environment is evolving. Thus, offline-trained segmentation models may lead to catastrophic forgetting of previously seen classes. Class-incremental learning (CIL) is designed to address the problem of catastrophic forgetting. While point clouds are common, we observe high similarity and unclear boundaries between different classes. Meanwhile, they are known to be imbalanced in class distribution. These lead to issues including misclassification between similar classes and the long-tail problem, which have not been adequately addressed in previous CIL methods. We thus propose ProtoGuard and PROPEL (Progressive Refinement Of PsEudo-Labels). In the base-class training phase, ProtoGuard maintains geometric and semantic prototypes for each class, which are combined into prototype features using an attention mechanism. In the novel-class training phase, PROPEL inherits the base feature extractor and classifier, guiding pseudo-label propagation and updates based on density distribution and semantic similarity. Extensive experiments show that our approach achieves remarkable results on both the S3DIS and ScanNet datasets, improving the mIoU of 3D point cloud segmentation by a maximum of 20.39% under the 5-step CIL scenario on S3DIS.

Related papers

• Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results [1.1328543389752008]
  We propose a plug and play framework for Open-Set Semantic (O3S)<n>By modeling the segmentation pipeline as a conditional Markov chain, we derive a novel regularizer term dubbed Conditional Channel Capacity Maximization (3CM)<n>We show that 3CM encourages the encoder to retain richer, label-dependent features, thereby enhancing the network's ability to distinguish and segment previously unseen categories.
  arXiv  Detail & Related papers  (2025-05-09T04:12:26Z)
• CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation [67.36817440834251]
  We propose a unified framework for textbfCLass-incremental textbfImbalance-aware textbf3DIS.<n>Our approach achieves state-of-the-art results, surpassing prior work by up to 16.76% mAP for instance segmentation and approximately 30% mIoU for semantic segmentation.
  arXiv  Detail & Related papers  (2025-02-24T18:58:58Z)
• Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation [50.51125319374404]
  We propose a novel self-training network InsTeacher3D to explore and exploit pure instance knowledge from unlabeled data. Experimental results on multiple large-scale datasets show that the InsTeacher3D significantly outperforms prior state-of-the-art semi-supervised approaches.
  arXiv  Detail & Related papers  (2024-06-24T16:35:58Z)
• Rethinking Few-shot 3D Point Cloud Semantic Segmentation [62.80639841429669]
  This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS) We focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built.
  arXiv  Detail & Related papers  (2024-03-01T15:14:47Z)
• Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization [64.36097398869774]
  Semi-supervised learning (SSL) has been an active research topic for large-scale 3D scene understanding. The existing SSL-based methods suffer from severe training bias due to class imbalance and long-tail distributions of the point cloud data. We introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively.
  arXiv  Detail & Related papers  (2024-01-13T04:16:40Z)
• Multi-modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation [47.81638388980828]
  We propose a simple yet effective scene-level weakly supervised point cloud segmentation method with a newly introduced multi-modality point affinity inference module. Our method outperforms the state-of-the-art by 4% to 6% mIoU on the ScanNet and S3DIS benchmarks.
  arXiv  Detail & Related papers  (2023-12-27T14:01:35Z)
• Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling [38.07637524378327]
  Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. Existing DA methods suffer from a substantial drop in performance when applied to a multi-class training setting. We propose a novel ReDB framework tailored for learning to detect all classes at once.
  arXiv  Detail & Related papers  (2023-07-16T04:34:11Z)
• Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding [61.30276576646909]
  We propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task.
  arXiv  Detail & Related papers  (2022-10-06T10:18:16Z)
• Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning [37.76664203157892]
  A fundamental challenge here lies in the large intra-class variations of local geometric structure, resulting in subclasses within a semantic class. We leverage this intuition and opt for maintaining an individual classifier for each subclass. Our hypothesis is also verified given the consistent discovery of semantic subclasses at no cost of additional annotations.
  arXiv  Detail & Related papers  (2022-05-06T11:07:36Z)
• Unsupervised Representation Learning for 3D Point Cloud Data [66.92077180228634]
  We propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. We conduct experiments on three downstream tasks which are 3D object classification, shape part segmentation and scene segmentation.
  arXiv  Detail & Related papers  (2021-10-13T10:52:45Z)
• Few-shot 3D Point Cloud Semantic Segmentation [138.80825169240302]
  We propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method. Our proposed method shows significant and consistent improvements compared to baselines in different few-shot point cloud semantic segmentation settings.
  arXiv  Detail & Related papers  (2020-06-22T08:05:25Z)

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.