Point-MF: Stabilizing One-Step Mean Flows for Single-Image Point Cloud Reconstruction

Abstract

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 large Mean-Flow jumps, we introduce Denoised Space Anchor (DSA), a set-distance auxiliary loss that anchors the denoised point set implied by the predicted velocity to the ground-truth geometry. On all 13 ShapeNet-R2N2 categories and Pix3D, Point-MF achieves strong reconstruction quality under the aligned point-set protocol, improving average CD and EMD over the evaluated point-cloud reconstruction baselines. It runs in 40.72 ms per sample on an RTX A4000, within the same latency order as the feedforward RGB2point baseline and substantially faster than iterative diffusion baselines such as PC${}^2$ and BDM. These results show that denoised-space geometric anchoring enables stable one-step Mean Flow directly in point-cloud space.

arXiv commentAccepted to ACCV 2026