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Point Cloud Generation from sUAS-Mounted iPhone Imagery: Performance Analysis [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 The rapidly growing use of sUAS technology and fast sensor developments continuously inspire mapping professionals to experiment with low-cost airborne systems. Smartphones has all the sensors used in modern airborne surveying systems, including GPS, IMU,
A. D. Ladai, J. Miller
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Point Cloud Generation with Continuous Conditioning
, 2022 Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper proposes a novel generative adversarial network (GAN) setup that generates 3D point cloud shapes conditioned on a continuous parameter.
Triess, Larissa T. +4 more
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NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer
Sensors, 2022 Self-attention networks have revolutionized the field of natural language processing and have also made impressive progress in image analysis tasks. Corrnet3D proposes the idea of first obtaining the point cloud correspondence in point cloud registration.
Xiaobo Hu +4 more
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Kronos: a workflow assembler for genome analytics and informatics. [PDF]
, 2017 BackgroundThe field of next-generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized.
Aniba, Radhouane +9 more
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, 2018 3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation.
Yue, Xiangyu +4 more
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Generative Models for 3D Point Clouds
, 2023 Point clouds are rich geometric data structures, where their three dimensional structure offers an excellent domain for understanding the representation learning and generative modeling in 3D space. In this work, we aim to improve the performance of point cloud latent-space generative models by experimenting with transformer encoders, latent-space flow
Kong, Lingjie +2 more
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Point cloud segmentation using hierarchical tree for architectural models
, 2018 Recent developments in the 3D scanning technologies have made the generation of highly accurate 3D point clouds relatively easy but the segmentation of these point clouds remains a challenging area.
Butt, Zain +3 more
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RPG: Learning Recursive Point Cloud Generation
, 2021 In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the target 3D model, the generation starts from a single point and gets expanded recursively to produce the high-resolution point cloud via a sequence of point expansion stages ...
Ko, Wei-Jan +5 more
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Remote SensingThe key to building a 3D point cloud map is to ensure the consistency and accuracy of point cloud data. However, the hardware limitations of LiDAR lead to a sparse and uneven distribution of point cloud data in the edge region, which brings many ...
Wenwen Li +5 more
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Using Auto-ML on Synthetic Point Cloud Generation
Applied SciencesAutomated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we
Moritz Hottong +2 more
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