23 papers · ranked by Valyu relevance
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…
Mahsa Partovi, Federico Iuricich
—The visualization of 3D point cloud data is essential in fields such as autonomous navigation, environmental monitoring, and disaster response, where tasks like object recognition, structural analysis, and spatiotemporal exploration rely on clear and effective visual representation. Despite advancements in AI-driven…
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…
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…
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…
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…
Yuhe Zhang, Zhikun Tu, Zhi Li, Jian Gao + 2 more
The points on the point clouds that can entirely outline the shape of the model are of critical importance, as they serve as the foundation for numerous point cloud processing tasks and are widely utilized in computer graphics and computer-aided design. This study introduces a novel method, RWoDSN, for extracting such…
Zhao, Pan, Yuan, Hui + 8 more
—Lossy compression of point clouds reduces storage and transmission costs; however, it inevitably leads to irreversible distortion in geometry structure and attribute information. To address these issues, we propose a unified geometry and attribute enhancement (UGAE) framework, which consists of three core components…
Ritvik Vasan, Alexandra Ferrante, Antoine Borensztejn, Christopher L. Frick + 7 more
A key challenge in understanding subcellular organization is quantifying interpretable measurements of intracellular structures with complex multi-piece morphologies in an objective, robust and generalizable manner. Here we introduce a morphology-appropriate representation learning framework that uses 3D rotation…
Zhiyuan Shi, Weiming Xu, Hao Meng, Gemine Vivone
Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm…
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…
Yangzhuo Chen, Fengjiao Guo, Jingang Liu, Siling Dai + 3 more
With the advancement of sensor technologies such as LiDAR and depth cameras, the significance of three-dimensional point cloud data in autonomous driving and environment sensing continues to increase.Point cloud registration stands as a fundamental task in constructing high-precision environmental models, with…
Lihong Yang, Shunqin Xu, Zhiqiang Yang, Jia He + 7 more
In response to the issues of slow convergence and the tendency to fall into local optima in traditional iterative closest point (ICP) point cloud registration algorithms, this study presents a fast registration algorithm for laser point clouds based on 3D scale-invariant feature transform (3D-SIFT) feature extraction.…
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…
Aline Bornand, Meinrad Abegg, Felix Morsdorf, Stefano Puliti + 2 more
Individual tree structure plays a key role in forest monitoring, biomass estimation, and ecological assessment. However, ground-based remote sensing methods such as terrestrial and mobile laser scanning frequently produce incomplete point clouds due to occlusion, particularly in the upper canopy. This limits the…
Daiki Someno, Koji Noshita
With the world facing escalating food demand, limited agricultural land, and environmental change, there is a growing need for data-driven sustainable agricultural management. Advances in sequencing and sensor networks have reduced costs of acquiring genomic and environmental data; however, collecting phenotypic data…
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…
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…