LBF:Learnable Bilateral Filter For Point Cloud Denoising

TL;DR

LBF introduces an end-to-end learnable bilateral filtering network for point cloud denoising that adaptively learns parameters for each point based on its geometric features, eliminating manual tuning and improving denoising quality.

Contribution

This is the first learnable bilateral filter for point cloud denoising that adapts parameters per point using geometric features and incorporates multi-scale perception.

Findings

LBF outperforms existing methods on synthetic datasets.

LBF achieves better preservation of geometric details.

LBF effectively reduces noise on real-scanned point clouds.

Abstract

Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising. However, it often involves continual yet manual parameter adjustment; this inconvenience discounts the efficiency and user experience to obtain satisfied denoising results. We propose LBF, an end-to-end learnable bilateral filtering network for point cloud denoising; to our knowledge, this is the first time. Unlike the conventional BF and its variants that receive the same parameters for a whole point cloud, LBF learns adaptive parameters for each point according its geometric characteristic (e.g., corner, edge, plane), avoiding remnant noise, wrongly-removed geometric details, and distorted shapes. Besides the learnable paradigm of BF, we have two cores to facilitate LBF. First, different from the local BF, LBF possesses a global-scale feature perception ability by exploiting multi-scale patches of each point. Second, LBF formulates a geometry-aware bi-directional projection loss, leading the denoising results to being faithful to their underlying surfaces. Users can apply our LBF without any laborious parameter tuning to achieve the optimal denoising results. Experiments show clear improvements of LBF over its competitors on both synthetic and real-scanned datasets.