SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation

TL;DR

This paper introduces SELECT, a scalable active learning method for LiDAR semantic segmentation that effectively handles class imbalance and large-scale data by selecting representative, uncertain, and diverse voxels and points.

Contribution

We propose a novel voxel-centric submodular active learning framework tailored for large-scale 3D LiDAR data, addressing class imbalance and scalability issues.

Findings

SELECT outperforms existing active learning methods on SemanticPOSS, SemanticKITTI, and nuScenes datasets.

The voxel-level selection reduces computational complexity while maintaining high segmentation accuracy.

The approach effectively balances classes, improving recognition of rare object categories.

Abstract

LiDAR-based semantic segmentation plays a vital role in autonomous driving by enabling detailed understanding of 3D environments. However, annotating LiDAR point clouds is extremely costly and requires assigning semantic labels to millions of points with complex geometric structures. Active Learning (AL) has emerged as a promising approach to reduce labeling costs by querying only the most informative samples. Yet, existing AL methods face critical challenges when applied to large-scale 3D data: outdoor scenes contain an overwhelming number of points and suffer from severe class imbalance, where rare classes have far fewer points than dominant classes. To address these issues, we propose SELECT, a voxel-centric submodular approach tailored for active LiDAR semantic segmentation. Our method targets both scalability problems and class imbalance through three coordinated stages. First, we perform Voxel-Level Submodular Subset Selection, which efficiently identifies representative voxels without pairwise comparisons, ensuring scalability. Second, we estimate Voxel-Level Model Uncertainty using Monte Carlo dropout, aggregating point-wise uncertainties to identify informative voxels. Finally, we introduce Submodular Maximization for Point-Level Class Balancing, which selects a subset of points that enhances label diversity, explicitly mitigating class imbalance. Experiments on SemanticPOSS, SemanticKITTI, and nuScenes benchmarks demonstrate that SELECT achieves superior performance compared to prior active learning approaches for 3D semantic segmentation.