Deep Hierarchical Learning for 3D Semantic Segmentation

The inherent structure of human cognition facilitates the hierarchical organization of semantic categories for three-dimensional objects, simplifying the visual world into distinct and manageable layers. A vivid example is observed in the animal-taxonomy domain, where distinctions are not only made between broader categories like birds and mammals but also within subcategories such as different bird species, illustrating the depth of human hierarchical processing. This observation bridges to the computational realm as this paper presents deep hierarchical learning (DHL) on 3D data. By formulating a probabilistic representation, our proposed DHL lays a pioneering theoretical foundation for hierarchical learning (HL) in visual tasks. Addressing the primary challenges in effectiveness and generality of DHL for 3D data, we 1) introduce a hierarchical regularization term to connect hierarchical coherence across the predictions with the classification loss; 2) develop a general deep learning framework with a hierarchical embedding fusion module for enhanced hierarchical embedding learning; and 3) devise a novel method for constructing class hierarchies in datasets with non-hierarchical labels, leveraging recent vision language models. A novel hierarchy quality indicator, CH-MOS, supported by questionnaire-based surveys, is developed to evaluate the semantic explainability of the generated class hierarchy for human understanding. Our methodology’s validity is confirmed through extensive experiments on multiple datasets for 3D object and scene point cloud semantic segmentation tasks, demonstrating DHL’s capability in parsing 3D data across various hierarchical levels. This evidence suggests DHL’s potential for broader applicability to a wide range of tasks.