PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
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TLDR
This work develops a simple data‐driven method for removing outliers and reducing noise in unordered point clouds using a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds.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 synthetic 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. Both the code and pre-trained networks can be found on the project page (https://github.com/mrakotosaon/pointcleannet).read more
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PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
TL;DR: A novel end-to-end network for robust point clouds processing, named PointASNL, which achieves state-of-the-art robust performance for classification and segmentation tasks on all datasets, and significantly outperforms previous methods on real-world outdoor SemanticKITTI dataset with considerate noise.
Proceedings ArticleDOI
PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling
TL;DR: PointASNL as discussed by the authors proposes an adaptive sampling (AS) module that re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud.
Book ChapterDOI
Points2Surf Learning Implicit Surfaces from Point Clouds
TL;DR: Points2Surf is presented, a novel patch-based learning framework that produces accurate surfaces directly from raw scans without normals at the cost of longer computation times and a slight increase in small-scale topological noise in some cases.
Proceedings ArticleDOI
Point Cloud Upsampling via Disentangled Refinement
TL;DR: This work proposes to disentangle the task based on its multi-objective nature and formulate two cascaded sub-networks, a dense generator and a spatial refiner, and designs a pair of local and global refinement units in the spatial refiners to evolve a coarse feature map.
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