PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (1901.01060v3)

Abstract: 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.

PointCleanNet: A Data-Driven Approach for Point Cloud Denoising and Outlier Removal

The paper "PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds" introduces a novel methodology for processing 3D point clouds, specifically focusing on mitigating noise and eliminating outliers. Point clouds, which are often derived from 3D scanning devices or image-based reconstruction techniques, are frequently degraded by various forms of noise and spurious data points—referred to as outliers. Traditional point cloud denoising methods heavily rely on assumptions about the local surface geometry or statistical characteristics of the noise, necessitating manual parameter tuning. In contrast, PointCleanNet offers a data-driven, automated solution using deep learning to perform these tasks efficiently and accurately across varied noise conditions without manual input tuning.

The core of the PointCleanNet framework is a two-stage deep neural network architecture, adapted from the PCPNet structure. Initially, the network discards outlier samples. Subsequently, it predicts correction vectors for the remaining points, effectively projecting them onto a cleaner representation of the original surfaces. This dual-phase design allows PointCleanNet to robustly handle point clouds with diverse noise levels and densities, making it especially suitable for large, densely sampled datasets where traditional methods might struggle.

A key feature of PointCleanNet is its parameter-free operation, which does not require prior knowledge of the noise model or surface characteristics. The method achieves this by training on synthetically generated datasets, which encompass a wide array of noise intensities and include both clean and deliberately corrupted point sets. During training, these in-built variances enable the network to learn effective noise-removal strategies that generalize well across unseen data. Moreover, the framework is designed to preserve high-curvature features without additional input concerning surface typology, lending to its versatility.

On the quantitative front, PointCleanNet demonstrated superior performance metrics across several benchmarks, compared to existing state-of-the-art methods. Specifically, it showed increased noise robustness and accuracy in surface reconstruction—even under severe noise perturbations—highlighting its practical applications for accurately reconstructing surfaces from extremely noisy data, as obtained through range scans and similar acquisition methods.

From a theoretical perspective, the implications of this research advance the discourse in point cloud processing by reducing reliance on handcrafted, assumption-based algorithms. The success of a neural network design working directly within the point domain suggests potential for further developments in data-driven approaches, which could extend beyond denoising and outlier removal to include tasks like shape completion and upsampling.

Looking forward, future explorations might include integrating PointCleanNet into broader geometry processing pipelines or extending its functionality to automatically enhance resolution in under-sampled areas. Improvements could also arise from developing mechanisms for training the network on unpaired datasets, which would widen its applicability to situations where access to clean samples is limited.

In summary, PointCleanNet represents a significant step in point cloud processing, empowering users with a tool that operates autonomously and adapts deftly to a range of challenging data conditions. Its reliance on learned features rather than explicit surface characteristics signifies a shift towards more intelligent and adaptable systems within the field of computer graphics and computational geometry.