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Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results

Wang Fang, Shirin Rahimi, Olivia Bennett, Sophie Carter, Mitra Hassani, Xu Lan, Omid Javadi, Lucas Mitchell

TL;DR

The paper tackles open-set semantic segmentation in 3D point clouds by introducing Conditional Channel Capacity Maximization (3CM), an information-theoretic regularizer derived from modeling segmentation as a conditional Markov chain. By maximizing the conditional mutual information between features and predictions given the class, 3CM encourages richer, label-conditioned representations without modifying the base architecture. Empirically, 3CM yields improved performance on unseen classes across multiple benchmarks (e.g., ShapeNet Part, Stanford Indoor3D) while preserving efficiency, and ablations identify a sweet spot at $\ ext{\lambda}=0.5$ and EMA factor around $\beta=0.995$. The work presents a principled, plug-in approach to robust open-set 3D segmentation with clear avenues for dynamic learning and more robust mutual-information estimation.

Abstract

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.

Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results

TL;DR

The paper tackles open-set semantic segmentation in 3D point clouds by introducing Conditional Channel Capacity Maximization (3CM), an information-theoretic regularizer derived from modeling segmentation as a conditional Markov chain. By maximizing the conditional mutual information between features and predictions given the class, 3CM encourages richer, label-conditioned representations without modifying the base architecture. Empirically, 3CM yields improved performance on unseen classes across multiple benchmarks (e.g., ShapeNet Part, Stanford Indoor3D) while preserving efficiency, and ablations identify a sweet spot at and EMA factor around . The work presents a principled, plug-in approach to robust open-set 3D segmentation with clear avenues for dynamic learning and more robust mutual-information estimation.

Abstract

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.
Paper Structure (13 sections, 5 equations, 3 figures, 5 tables)

This paper contains 13 sections, 5 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Notation
  4. Methodology
  5. Segmentation as a Markov Chain
  6. Necessity of Maximizing Conditional Channel Capacity
  7. Conditional Channel Capacity Maximization
  8. Experimental Results
  9. Ablation Study
  10. Effectiveness of the 3CM
  11. The necessary of using EMA to update the Q
  12. Discussion
  13. Concluding Remarks

Figures (3)

  • Figure 1: Semantic segmentation results on Stanford-indoor3d dataset with (b) and without (a) 3CM regularization. The fine-tuned model demonstrates significantly improved performance on unseen classes when 3CM is applied, which aligns with the results in Table \ref{['Tab:SegResults']}, confirming the effectiveness of 3CM.
  • Figure 2: The objective value over the unseen class, the value converge with respect to the number of the training steps.
  • Figure 3: Segmentation results using varying regularization parameter $\lambda$: (a) $\lambda$=0.1, (b) $\lambda$=0.3, (c) $\lambda$=0.5, and (d) $\lambda$=0.7.