PointCRO: Class-Competition Relative Optimization for Point Cloud Segmentation

Current regularization methods for point cloud segmentation typically rely on surrogate objectives defined over feature geometry or output distributions. Our diagnostics show that improvements in these objectives do not consistently translate into higher segmentation accuracy, motivating explicit modeling of point-wise class competition. We observe that many prediction errors arise from imbalanced competition among the ground-truth class, the most competitive incorrect class (TopWrong), and the remaining classes. To address this issue, we propose PointCRO, a prediction-state-aware fine-tuning framework that combines reward-based output regulation with representation-level regularization. Specifically, Class-Group Relative Optimization (CGRO) formulates each point prediction as a class competition and uses competition-aware relative rewards to rebalance the ground-truth, TopWrong, and remaining classes, directly correcting unfavorable prediction states rather than optimizing generic output statistics. Reward-guided Feature-level Kullback–Leibler (RFKL) regularization transfers these reward-induced preferences to pairwise feature relations, aligning representation structure with the desired competition dynamics and stabilizing the resulting prediction correction. Experiments across multiple benchmarks demonstrate consistent improvements in semantic segmentation performance. Endpoint prediction-transition analyses further show that PointCRO better balances error correction and the preservation of correct predictions, supporting prediction-state-aware class competition as a more direct optimization target and reducing the mismatch between surrogate regularization gains and segmentation improvements.