PRISM: Point Cloud Restoration for Impaired Single-Photon Data via Latent Point Flow Matching

Single-photon imaging enables 3D sensing under low illumination and at long range, making point-cloud restoration essential for converting sparse, contaminated measurements into usable geometry. However, existing object-level datasets offer limited shape diversity and paired reference geometry for evaluating restoration under controlled acquisition conditions. Spatially non-uniform sampling, missing surface observations, and spurious detections further complicate recovery by making geometric evidence unevenly reliable. To address these challenges, we introduce SPORE, a physics-grounded simulated benchmark spanning 55 ShapeNet categories and three corruption regimes, constructed using ray-based acquisition and Poisson photon statistics. We also propose PRISM, a hierarchical latent flow-matching framework that selectively repairs unreliable local anchors and retains their reliability and correction information to guide subsequent global–local restoration. On SPORE, PRISM achieves the best results on three of four metrics under Mid corruption and all four under High corruption, reducing HD95 by 27.6% and improving [email protected] by 22.9% relative to the strongest competing result for each metric in the High regime. These findings support reliability-informed restoration as an effective approach to recovering precise geometry under severe simulated single-photon degradation.