Revisiting Tradition and Beyond: A Customized Bilateral Filtering Framework for Point Cloud Denoising

Deep learning-based methods have become the dominant solution for point cloud denoising, offering strong generalization capabilities through data-driven training. However, traditional methods, despite their drawbacks of heavy parameter tuning and weak generalization, retain unique advantages in interpretability and theoretical robustness. This complementarity motivates us to explore a hybrid solution that leverages data-driven paradigms to overcome the performance constraints of traditional methods. In this paper, we revisit the classic bilateral filter (BF) as a case study and identify three key limitations hindering its performance: excessive parameter tuning, suboptimal neighborhood quality, and fixed parameters across the entire model. To address them, we propose CustomBF, a novel framework for customizing BF components at a per-point level. CustomBF employs multigraph encoders and a mutual guidance strategy to analyze local patches, enabling the customization of BF components including center point normal, neighborhood point coordinates, Gaussian function parameters, and neighborhood radius for each point. Experimental results demonstrate that this component-customized bilateral filter outperforms state-of-the-art methods and achieves robust denoising even in complex scenarios. It highlights the potential of hybrid methods to extend the applicability and effectiveness of traditional techniques.