Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds

A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.Comment: To be published in proceedings of 3DIMPVT 201

Object Recognition in Noisy RGB-D Data

The object recognition task on 3D scenes is a growing research field that faces some problems relative to the use of 3D point clouds. In this work, we focus on dealing with noisy clouds through the use of the Growing Neural Gas (GNG) network filtering algorithm. Another challenge is the selection of the right keypoints detection method, that allows to identify a model into a scene cloud. The GNG method is able to represent the input data with a desired resolution while preserving the topology of the input space. Experiments show how the introduction of the GNG method yields better recognitions results than others filtering algorithms when noise is present.The object recognition task on 3D scenes is a growing research field that faces some problems relative to the use of 3D point clouds. In this work, we focus on dealing with noisy clouds through the use of the Growing Neural Gas (GNG) network filtering algorithm. Another challenge is the selection of the right keypoints detection method, that allows to identify a model into a scene cloud. The GNG method is able to represent the input data with a desired resolution while preserving the topology of the input space. Experiments show how the introduction of the GNG method yields better recognitions results than others filtering algorithms when noise is present

On the feedback from super stellar clusters. I. The structure of giant HII regions and HII galaxies

We review the structural properties of giant extragalactic HII regions and HII galaxies based on 2D hydrodynamic calculations, and propose an evolutionary sequence that accounts for their observed detailed structure. The model assumes a massive and young stellar cluster surrounded by a large collection of clouds. These are thus exposed to the most important star-formation feedback mechanisms: photoionization and the cluster wind. The models show how the two feedback mechanisms compete in the disruption of clouds and lead to two different hydrodynamic solutions: The storage of clouds into a long lasting ragged shell that inhibits the expansion of the thermalized wind, and the steady filtering of the shocked wind gas through channels carved within the cloud stratum. Both solutions are claimed to be concurrently at work in giant HII regions and HII galaxies, causing their detailed inner structure. This includes multiple large-scale shells, filled with an X-ray emitting gas, that evolve to finally merge with each other, giving the appearance of shells within shells. The models also show how the inner filamentary structure of the giant superbubbles is largely enhanced with matter ablated from clouds and how cloud ablation proceeds within the original cloud stratum. The calculations point at the initial contrast density between the cloud and the intercloud media as the factor that defines which of the two feedback mechanisms becomes dominant throughout the evolution. Animated version of the models can be found at http://www.iaa.csic.es/\~{}eperez/ssc/ssc.html.Comment: 28 pages, 10 figures, accepted for publication in the ApJ. Animated version of the models can be found at http://www.iaa.csic.es/\~{}eperez/ssc/ssc.htm

Detection of a new, low-brightness supernova remnant possibly associated with EGRET sources

We report on the discovery of a shell-type supernova remnant in the southern sky. It is a large (8*8), low-brightness source with a nonthermal radio spectrum, which requires background filtering to isolate it from the diffuse background emission of the Galaxy. Three 3EG gamma-ray sources are spatially correlated with the radio structure. We have made 21-cm line observations of the region and found that two of these sources are coincident with HI clouds. We propose that the gamma-ray emission is the result of hadronic interactions between high-energy protons locally accelerated at the remnant shock front and atomic nuclei in the ambient clouds.Comment: 6 pages, 5 figure

Ground Profile Recovery from Aerial 3D LiDAR-based Maps

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc