AI Insight
Researchers developed a new computational framework called GIC-Reg that improves the reconstruction of 3D environments from single-photon LiDAR by combining both geometric shape data and light intensity information to align multiple viewpoints without requiring precise sensor position data. The method successfully handles sparse, noisy point clouds typical of single-photon detection, reducing rotation errors by approximately 36% compared to existing learning-based approaches in heavily degraded conditions. Testing on both synthetic benchmarks and real-world data collected at 80 meters distance demonstrated more reliable alignment and reconstruction than current methods.
Why it matters
This advance could enhance 3D sensing capabilities in challenging conditions such as long-range surveillance, low-light environments, and situations with poorly reflective surfaces where traditional LiDAR struggles. The approach addresses a critical limitation in single-photon sensing technology by enabling accurate multi-view fusion without precise sensor positioning, expanding potential applications in autonomous navigation, remote sensing, and environmental monitoring.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: Single-photon light detection and ranging (LiDAR) extends active three-dimensional sensing at the fundamental level and has found applications in extreme environments involving long-range operation, low-reflectance targets, and adverse visibility. However, the acquired measurements often give rise to single-photon point clouds that are sparse, spatially non-uniform, and corrupted by outliers and depth distortions, making multi-view registration challenging especially when sensor poses are not accurately known. In this work, we present a geometry-intensity coupled registration framework (GIC-Reg) of pose-free multi-view fusion for single-photon sensing. It is established by combining physical-aware preprocessing, joint geometry-intensity grid feature aggregation, global matching, and local ambiguity disambiguation to estimate inter-view rigid transformations and hence to construct a globally consistent reconstruction. On the synthetic benchmark, it admits the lowest relative rotation error (RRE), relative translation error, and root mean square error across all background-noise and dropout rates, in comparison to baselines. Notably, under the most degraded dropout, it reduces the RRE from $13.167^circ$ to $8.459^circ$ compared with the learning-based baseline. Furthermore, experimental results on real multi-view data acquired at about 80~m show that it achieves more reliable global orientation and local alignment. Our results show that photon intensity provides an effective physical cue for stabilizing multiview registration in single-photon point cloud, and thus our work aids significant progress in exploring practical utility of single-photon sensing.
Source: Intensity-guided pose-free multiview fusion for single photon sensing