Label-efficient Single Photon Images Classification via Active Learning

TL;DR

This paper introduces an active learning framework for classifying single-photon LiDAR images, significantly reducing annotation efforts while maintaining high accuracy in challenging environments.

Contribution

It presents the first active learning approach tailored for single-photon image classification, incorporating imaging condition-aware sampling and synthetic augmentation.

Findings

Achieves 97% accuracy with only 1.5% labeled data on synthetic datasets.

Maintains 90.63% accuracy with 8% labeled data on real-world datasets.

Outperforms all baseline methods in label efficiency and accuracy.

Abstract

Single-photon LiDAR achieves high-precision 3D imaging in extreme environments through quantum-level photon detection technology. Current research primarily focuses on reconstructing 3D scenes from sparse photon events, whereas the semantic interpretation of single-photon images remains underexplored, due to high annotation costs and inefficient labeling strategies. This paper presents the first active learning framework for single-photon image classification. The core contribution is an imaging condition-aware sampling strategy that integrates synthetic augmentation to model variability across imaging conditions. By identifying samples where the model is both uncertain and sensitive to these conditions, the proposed method selectively annotates only the most informative examples. Experiments on both synthetic and real-world datasets show that our approach outperforms all baselines and achieves high classification accuracy with significantly fewer labeled samples. Specifically, our approach achieves 97% accuracy on synthetic single-photon data using only 1.5% labeled samples. On real-world data, we maintain 90.63% accuracy with just 8% labeled samples, which is 4.51% higher than the best-performing baseline. This illustrates that active learning enables the same level of classification performance on single-photon images as on classical images, opening doors to large-scale integration of single-photon data in real-world applications.