22 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)…
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…
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…
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…
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…
Chunyang Fu, Ge Li, Wei Gao, Shiqi Wang + 2 more
Recently, deep learning has significantly advanced the performance of point cloud geometry compression. However, the learning-based lossless attribute compression of point clouds with varying densities is underexplored. In this paper, we develop a learning-based framework, namely DALD-PCAC that leverages Levels of…
Yueyu Hu, Ran Gong, Qi Sun, Yao Wang
Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the…
Maurice Quach, Giuseppe Valenzise, Frédéric Dufaux
Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent advances in point cloud representation learning. Point clouds can be interpreted as 2D manifolds in 3D space. Specifically, we fold a 2D grid…
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…
Siyang Yu, Si Sun, Wei Yan, Guangshuai Liu + 2 more
'Nikolaos Doulamis'] As a kind of information-intensive 3D representation, point cloud rapidly develops in immersive applications, which has also sparked new attention in point cloud compression. The most popular dynamic methods ignore the characteristics of point clouds and use an exhaustive neighborhood search, which…
Jaka Kordež, Matija Marolt, Ciril Bohak, Markus Hollaus
Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way the points are rendered by either rendering them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to…
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…
Yanjun Zhang, Imran Ahmed
Reason Pedestrian recognition has great practical value and is a vital step toward applying path planning and intelligent obstacle avoidance in autonomous driving. In recent years, laser radar has played an essential role in pedestrian detection and recognition in unmanned driving. More accurate high spatial dimension…
Ayan Chaudhury, Christophe Godin
Skeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing…
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…
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…
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…
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…
A. Christine Swanson, Trina Merrick, Andrei Abelev, Robert Liang + 3 more
Three-dimensional forest structure plays an important role in processes such as biomass accumulation and fire spread and provides wildlife with habitat and foraging spaces. Advances in lidar mapping have improved forest structure quantification at local to global scales. However, point cloud density may have effects on…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
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…