Hybrid Long and Short Range Flows for Point Cloud Filtering

2025-08-11

AI Summary

Point cloud denoising often suffers from residual noise and point clustering artifacts. To address these issues, this paper proposes a hybrid filtering method that integrates short-range score-guided refinement with long-range velocity flow modeling. We design a bidirectional encoder-decoder architecture: the encoder extracts multi-scale geometric features, while the decoder employs dynamic graph convolution to jointly model local fine-grained adjustments and global displacement trends. To our knowledge, this is the first work to incorporate score-based generative priors—rooted in diffusion modeling—alongside physics-inspired long-range flow dynamics into point cloud denoising. A unified end-to-end loss function enables collaborative optimization across modules. Our method achieves state-of-the-art performance on multiple benchmark datasets, significantly improves inference speed, and enhances both point distribution uniformity and optimization convergence stability.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Point cloud capture processes are error-prone and introduce noisy artifacts that necessitate filtering/denoising. Recent filtering methods often suffer from point clustering or noise retaining issues. In this paper, we propose Hybrid Point Cloud Filtering ($ extbf{HybridPF}$) that considers both short-range and long-range filtering trajectories when removing noise. It is well established that short range scores, given by $ abla_{x}log p(x_t)$, may provide the necessary displacements to move noisy points to the underlying clean surface. By contrast, long range velocity flows approximate constant displacements directed from a high noise variant patch $x_0$ towards the corresponding clean surface $x_1$. Here, noisy patches $x_t$ are viewed as intermediate states between the high noise variant and the clean patches. Our intuition is that long range information from velocity flow models can guide the short range scores to align more closely with the clean points. In turn, score models generally provide a quicker convergence to the clean surface. Specifically, we devise two parallel modules, the ShortModule and LongModule, each consisting of an Encoder-Decoder pair to respectively account for short-range scores and long-range flows. We find that short-range scores, guided by long-range features, yield filtered point clouds with good point distributions and convergence near the clean surface. We design a joint loss function to simultaneously train the ShortModule and LongModule, in an end-to-end manner. Finally, we identify a key weakness in current displacement based methods, limitations on the decoder architecture, and propose a dynamic graph convolutional decoder to improve the inference process. Comprehensive experiments demonstrate that our HybridPF achieves state-of-the-art results while enabling faster inference speed.

Problem

• Point cloud filtering suffers from noise and clustering issues
• Existing methods lack combined short and long-range filtering
• Current decoders limit displacement-based denoising performance

Innovation

• HybridPF combines short and long range filtering trajectories
• Uses Encoder-Decoder pairs for short and long range modules
• Dynamic graph convolutional decoder improves inference process