Point-MF: Stabilizing One-Step Mean Flows for Single-Image Point Cloud Reconstruction
Single-image point cloud reconstruction requires recovering complete object-level geometry, including occluded regions, from a single RGB image. Diffusion- and flow-based reconstructors can model this ambiguity, but their iterative sampling requires many network function evaluations, while naive one-step point-space updates often produce outliers and density imbalance. We propose Point-MF, a one-step conditional Mean-Flow framework for geometry-only point cloud reconstruction. Point-MF predicts an interval-averaged velocity field directly in point-cloud space and reconstructs the output with a single network function evaluation, without training a point-cloud autoencoder. To stabilize lar...
| Authors | Yuta Baba, Keiji Yanai |
|---|---|
| Primary category | cs.CV |
| Published | 27 April 2026 |
| Last updated at source | 2 October 2026 |
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