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Text-guided class-incremental point cloud semantic segmentation with category distribution constraint
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AI
DOI：10.1016/j.engappai.2026.114984.png)
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Abstract
En 中文
Class-incremental light detection and ranging (LiDAR) semantic segmentation refers to maintaining recognition performance on previously learned categories while gradually introducing new categories during learning. This task is particularly challenging due to the sparse, irregular nature of point clouds and the severe class imbalance in large-scale scenes. Existing methods mainly rely on single-modality point-cloud features, which provide limited semantic priors and are vulnerable to category distribution drift across incremental steps. Moreover, its global knowledge distillation tends to make the features of old and novel classes overlap, resulting in confusion between similar categories. In this paper, we propose a novel incremental learning method that effectively integrates multimodal information to enhance the distinction between categories. We introduce textual information and deeply couple the incremental point cloud with text features through a cross-modal attention mechanism, effectively achieving the semantic complementarity across modalities. To further enhance the discriminability of previously learned category boundaries, we propose a historical multi-prototype distribution consistency constraint. In addition, we propose an orthogonality constraint between novel and previous knowledge to mitigate the performance degradation caused by conflicts introduced when learning new categories incrementally. Extensive experiments on the autonomous driving dataset show that our proposed method achieves the state-of-the-art results.
Keywords:
class-incremental learning
point cloud semantic segmentation
multimodal information
cross-modal attention
category distribution constraint
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