23 papers · ranked by Valyu relevance
Weiping Liu, Jia Sun, Wanyi Li, Ting Hu + 1 more
Point cloud is a widely used 3D data form, which can be produced by depth sensors, such as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and irregular, many researchers focused on the feature engineering of the point cloud. Being able to learn complex hierarchical structures, deep learning has…
Elena Camuffo, Daniele Mari, Simone Milani, Santiago Marco
Recent advancements in self-driving cars, robotics, and remote sensing have widened the range of applications for 3D Point Cloud (PC) data. This data format poses several new issues concerning noise levels, sparsity, and required storage space; as a result, many recent works address PC problems using Deep Learning (DL)…
Emil Dumic, Anamaria Bjelopera, Andreas Nüchter, Steve Vanlanduit
In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video…
Chengzhi Deng, Huaipei Wang, Zhaoming Wu, Xiaowei Sun + 3 more
Point cloud classification and segmentation are key technologies for 3D perception and scene understanding, whose accuracy and efficiency directly affect the performance of high-level applications such as 3D modeling, object recognition, and intelligent interaction. Existing methods still exhibit obvious deficiencies…
Siheng Chen, Dong Tian, Chen Feng, Anthony Vetro + 1 more
—To reduce cost in storing, processing and visualizing a large-scale point cloud, we consider a randomized resampling strategy to select a representative subset of points while preserving application-dependent features. The proposed strategy is based on graphs, which can represent underlying surfaces and lend…
Hoda Roodaki, Mahdi Nazm Bojnordi
—The ever-increasing demand for 3D modeling in the emerging immersive applications has made point clouds an essential class of data for 3D image and video processing. Treebased structures are commonly used for representing point clouds where pointers are used to realize the connection between nodes. Tree-based…
Ying He, Bin Liang, Jun Yang, Shunzhi Li + 1 more
The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process of accurate registration of 3D point cloud data. The algorithm requires a proper initial value and the approximate registration of two point clouds to prevent the algorithm from falling into local extremes, but in the actual…
Amit K. Vishwakarma, KS Subrahamanian Moosath
—In this paper, we introduce a novel method for comparing 3D point clouds, a critical task in various machine learning applications. By interpreting point clouds as samples from underlying probability density functions, the statistical manifold structure is given to the space of point clouds. This manifold structure…
Huang Zhang, Changshuo Wang, Shengwei Tian, Baoli Lu + 3 more
'Xin Ning' 'Xiao Bai'] Abstract—In recent years, point cloud representation has become one of the research hotspots in the field of computer vision, and has been widely used in many fields, such as autonomous driving, virtual reality, robotics, etc. Although deep learning techniques have achieved great success in…
Faranak Tohidi, Manoranjan Paul, Anwaar Ulhaq, Subrata Chakraborty + 1 more
A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially…
Ahmed El Khazari, Yue Que, Thai Leang Sung, Hyo Jong Lee
Point cloud registration is a key problem in computer vision applications and involves finding a rigid transform from a point cloud into another such that they align together. The iterative closest point (ICP) method is a simple and effective solution that converges to a local optimum. However, despite the fact that…
Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran
Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity. However, its inherent unordered and irregular nature, exacerbated by sensor noise and occlusions, introduces unique challenges for machine learning based methodologies. To combat…
Emmanuele Barberi, Felice Sfravara, Filippo Cucinotta
—Point cloud registration is a central theme in computer vision, with alignment algorithms continuously improving for greater robustness. Commonly used methods evaluate Euclidean distances between point clouds and minimize an objective function, such as Root Mean Square Error (RMSE). However, these approaches are most…
A.J. Khalid, Jaiaid Mobin, Sumanth Rao Appala, Avinash Maurya + 4 more
An autonomous vehicle can generate several terabytes of sensor data per day. A significant portion of this data consists of 3D point clouds produced by depth sensors such as LiDARs. This data must be transferred to cloud storage, where it is utilized for training machine learning models or conducting analyses, such as…
Atsushi Hayashi, Nobuo Kochi, Kunihiro Kodama, Sachiko Isobe + 1 more
This research proposes a novel technique for acquiring a large amount of high-density, high-precision 3D point cloud data for plants. We propose two methods, multi-masked matching (MMM) and the closed-loop coarse-to-fine method (CLCFM). The proposed approach addresses challenges in reconstructing plant 3D point clouds…
Hyungki Kim, Duhwan Mun
Technology to recognize the type of component represented by a point cloud is required in the reconstruction process of an as-built model of a process plant based on laser scanning. The reconstruction process of a process plant through laser scanning is divided into point cloud registration, point cloud segmentation…
Corinne Jones, Sophie Clayton, François Ribalet, E. Virginia Armbrust + 1 more
Automated, ship-board flow cytometers provide high-resolution maps of phytoplankton composition over large swaths of the world’s oceans. They therefore pave the way for understanding how environmental conditions shape community structure. Identification of community changes along a cruise transect commonly segments the…
Mohammed Baragilly, Daniel J. Nieves, David J. Williamson, Ruby Peters + 1 more
Single-molecule localisation microscopy produces data in the form of point-clouds. Here, we present a tool for assessing the similarity of two such point-clouds, which, unlike measures such as co-localisation, is insensitive to differences that are not preserved between data sets. The presented method can determine…
Authors not listed
The screening of chemical libraries is an essential starting point in the drug discovery process. While some researchers desire a more thorough screening of drug targets against a narrower scope of molecules, it is not uncommon for diverse screening sets to be favored during early stages of drug discovery. However, a…
Arthur Porto, Sara M. Rolfe, A. Murat Maga
Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable…
Authors not listed
Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full…
Diana Mindroc-Filimon, Dominic Helmerich, Patrick Salome, Markus Sauer + 1 more
Single-molecule localization microscopy (SMLM) can resolve intracellular structures down to the nanoscale, but often produces sparse and incomplete data. Particle averaging (PA) can aid with the reconstruction of complete structures, but traditional PA methods can suffer from template bias or the high computational…
utiyama masahiro, fukuyama tsutom
Formation and growth of water droplets were studied using a semi-real scale experimental system; the main component of the system was a 430 m vertical shaft used to perform cloud formation experiments at a spatial scale close to that of the real atmosphere. By generating an updraft with humid air, a cloud was observed…