1 research outputs found
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Point clouds obtained with 3D scanners or by image-based reconstruction
techniques are often corrupted with significant amount of noise and outliers.
Traditional methods for point cloud denoising largely rely on local surface
fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on
statistical assumptions about the underlying noise model. In contrast, we
develop a simple data-driven method for removing outliers and reducing noise in
unordered point clouds. We base our approach on a deep learning architecture
adapted from PCPNet, which was recently proposed for estimating local 3D shape
properties in point clouds. Our method first classifies and discards outlier
samples, and then estimates correction vectors that project noisy points onto
the original clean surfaces. The approach is efficient and robust to varying
amounts of noise and outliers, while being able to handle large densely-sampled
point clouds. In our extensive evaluation, both on synthesic and real data, we
show an increased robustness to strong noise levels compared to various
state-of-the-art methods, enabling accurate surface reconstruction from
extremely noisy real data obtained by range scans. Finally, the simplicity and
universality of our approach makes it very easy to integrate in any existing
geometry processing pipeline