PUFM++: Point Cloud Upsampling via Enhanced Flow Matching

• URL: http://arxiv.org/abs/2512.20988v1
• Date: Wed, 24 Dec 2025 06:30:42 GMT
• Title: PUFM++: Point Cloud Upsampling via Enhanced Flow Matching
• Authors: Zhi-Song Liu, Chenhang He, Roland Maier, Andreas Rupp,
• Abstract summary: PUFM++ is an enhanced flow-matching framework for reconstructing point clouds from sparse, noisy, and partial observations.<n>We introduce a two-stage flow-matching strategy that first learns a direct, straight-path flow from sparse inputs to dense targets, and then refines it using noise-perturbed samples to approximate the terminal marginal distribution better.<n>Experiments on synthetic benchmarks and real-world scans show that PUFM++ sets a new state of the art in point cloud upsampling.
• Score: 15.738247394527024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in generative modeling have demonstrated strong promise for high-quality point cloud upsampling. In this work, we present PUFM++, an enhanced flow-matching framework for reconstructing dense and accurate point clouds from sparse, noisy, and partial observations. PUFM++ improves flow matching along three key axes: (i) geometric fidelity, (ii) robustness to imperfect input, and (iii) consistency with downstream surface-based tasks. We introduce a two-stage flow-matching strategy that first learns a direct, straight-path flow from sparse inputs to dense targets, and then refines it using noise-perturbed samples to approximate the terminal marginal distribution better. To accelerate and stabilize inference, we propose a data-driven adaptive time scheduler that improves sampling efficiency based on interpolation behavior. We further impose on-manifold constraints during sampling to ensure that generated points remain aligned with the underlying surface. Finally, we incorporate a recurrent interface network~(RIN) to strengthen hierarchical feature interactions and boost reconstruction quality. Extensive experiments on synthetic benchmarks and real-world scans show that PUFM++ sets a new state of the art in point cloud upsampling, delivering superior visual fidelity and quantitative accuracy across a wide range of tasks. Code and pretrained models are publicly available at https://github.com/Holmes-Alan/Enhanced_PUFM.

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