Add to ORKGInternational audienceThis article addresses the problem of denoising 3D data from LIDAR. It is a step often required to allow a good reconstruction of surfaces represented by point clouds. In this paper, we present an original algorithm inspired by a recent method developed by (Buades and Morel, 2005) in the field of image processing, the Non Local Denoising (NLD). With a local geometric descriptor, we look for points that have similarities in order to reduce noise while preserving the surface details. We describe local geometry by MLS surfaces and we use a local reference frame invariant by rotation for denoising points. We present our results on synthetic and real data
Recent advances in scanning technologies, in particular devices that extract depth through active se...
The faithful reconstruction of 3-D models from irregular and noisy point samples is a task central t...
The Smart City concept requires new, fast methods for collection of 3-D data representing features o...
This article addresses the problem of denoising 3D data from LIDAR. It is a step often required to a...
International audienceThis article addresses the problem of denoising 3D data from LIDAR. It is a st...
International audienceDenoising surfaces is a a crucial step in the surface processing pipeline. Thi...
We present a new method for noise removal on static and time-varying range data. Our approach predi...
The increased availability of point cloud data in recent years has lead to a concomitant requirement...
Many point cloud acquisition methods, e.g. multi-viewpoint image stereo matching and acquisition of ...
Abstract We present a simple denoising technique for geometric data rep-resented as a semiregular me...
3D point clouds commonly contain positional errors which can be regarded as noise. We propose a poin...
Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have ...
Abstract: Different from previous local smoothing filters based on local geometry signal, a novel d...
The semantic interpretation using point clouds, especially regarding light detection and ranging (Li...
Since the introduction of lidar technology, lidar data has been used in a wide range of applications...
Recent advances in scanning technologies, in particular devices that extract depth through active se...
The faithful reconstruction of 3-D models from irregular and noisy point samples is a task central t...
The Smart City concept requires new, fast methods for collection of 3-D data representing features o...
This article addresses the problem of denoising 3D data from LIDAR. It is a step often required to a...
International audienceThis article addresses the problem of denoising 3D data from LIDAR. It is a st...
International audienceDenoising surfaces is a a crucial step in the surface processing pipeline. Thi...
We present a new method for noise removal on static and time-varying range data. Our approach predi...
The increased availability of point cloud data in recent years has lead to a concomitant requirement...
Many point cloud acquisition methods, e.g. multi-viewpoint image stereo matching and acquisition of ...
Abstract We present a simple denoising technique for geometric data rep-resented as a semiregular me...
3D point clouds commonly contain positional errors which can be regarded as noise. We propose a poin...
Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have ...
Abstract: Different from previous local smoothing filters based on local geometry signal, a novel d...
The semantic interpretation using point clouds, especially regarding light detection and ranging (Li...
Since the introduction of lidar technology, lidar data has been used in a wide range of applications...
Recent advances in scanning technologies, in particular devices that extract depth through active se...
The faithful reconstruction of 3-D models from irregular and noisy point samples is a task central t...
The Smart City concept requires new, fast methods for collection of 3-D data representing features o...