Efficient Point Cloud Upsampling via Local Flow Matching

Generative point cloud upsampling becomes increasingly expensive as the requested output point count grows. We present FLUSSO, a flow-matching framework that separates sparse global geometric reasoning from dense local generation. A sparse encoder extracts global context, while a shared velocity field evolves particles within adaptive anchor-centered supports. To train these local flows, sliced optimal-transport flow matching constructs candidate correspondences through one-dimensional projections, scores their transport costs in three dimensions, and softly weights multiple low-cost coupling losses. Complementary multi-step local endpoint distribution matching supervises the terminal geometry reached by differentiable model rollouts. At inference, local predictions are merged and subsampled when necessary, allowing one model to support different output cardinalities. Experiments on PU1K and PUGAN demonstrate competitive reconstruction quality and favorable latency and memory efficiency under the evaluated protocols. At 16× upsampling on PU1K, FLUSSO reduces squared Hausdorff and point-to-surface errors by 42.0% and 26.0%, respectively, compared with PUFM evaluated using the same metric pipeline. A model trained at 4× also generalizes to the evaluated unseen ratios from 5× to 32× without retraining.