Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results
This addresses the challenge of recognizing out-of-distribution objects in 3D point clouds for applications like autonomous driving, but it appears incremental as it builds on existing segmentation methods with a novel regularizer.
The paper tackles the problem of open-set semantic segmentation in 3D point clouds, where models must label known categories and detect unseen classes, by proposing a plug-and-play framework with a Conditional Channel Capacity Maximization regularizer that enhances the ability to segment novel objects, as shown in experimental results.
Point-cloud semantic segmentation underpins a wide range of critical applications. Although recent deep architectures and large-scale datasets have driven impressive closed-set performance, these models struggle to recognize or properly segment objects outside their training classes. This gap has sparked interest in Open-Set Semantic Segmentation (O3S), where models must both correctly label known categories and detect novel, unseen classes. In this paper, we propose a plug and play framework for O3S. By modeling the segmentation pipeline as a conditional Markov chain, we derive a novel regularizer term dubbed Conditional Channel Capacity Maximization (3CM), that maximizes the mutual information between features and predictions conditioned on each class. When incorporated into standard loss functions, 3CM encourages the encoder to retain richer, label-dependent features, thereby enhancing the network's ability to distinguish and segment previously unseen categories. Experimental results demonstrate effectiveness of proposed method on detecting unseen objects. We further outline future directions for dynamic open-world adaptation and efficient information-theoretic estimation.