  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.
