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pro vyhledávání: '"Ledoux, Hugo"'
We introduce CityJSON Text Sequences (CityJSONSeq in short), a format based on JSON Text Sequences and CityJSON. CityJSONSeq was added to the CityJSON version 2.0 standard to allow us to stream very large 3D city models. The main idea is to decompose
Externí odkaz:
http://arxiv.org/abs/2407.00017
This paper presents a new algorithm for filling holes in Level of Detail 2 (LoD2) building mesh models, addressing the challenges posed by geometric inaccuracies and topological errors. Unlike traditional methods that often alter the original geometr
Externí odkaz:
http://arxiv.org/abs/2404.15892
Autor:
Powałka, Leon, Poon, Chris, Xia, Yitong, Meines, Siebren, Yan, Lan, Cai, Yuduan, Stavropoulou, Gina, Dukai, Balázs, Ledoux, Hugo
When it comes to storing 3D city models in a database, the implementation of the CityGML data model can be quite demanding and often results in complicated schemas. As an example, 3DCityDB, a widely used solution, depends on a schema having 66 tables
Externí odkaz:
http://arxiv.org/abs/2307.06621
Traditional MVS methods have good accuracy but struggle with completeness, while recently developed learning-based multi-view stereo (MVS) techniques have improved completeness except accuracy being compromised. We propose depth discontinuity learnin
Externí odkaz:
http://arxiv.org/abs/2203.01391
Publikováno v:
In Building and Environment 1 November 2024 265
We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic seg
Externí odkaz:
http://arxiv.org/abs/2202.03209
While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open problem. In this paper, we present a novel framework for reconstructing co
Externí odkaz:
http://arxiv.org/abs/2112.13142
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 179, September 2021, Pages 108-120
Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban
Externí odkaz:
http://arxiv.org/abs/2103.00355
Autor:
Kölle, Michael, Laupheimer, Dominik, Schmohl, Stefan, Haala, Norbert, Rottensteiner, Franz, Wegner, Jan Dirk, Ledoux, Hugo
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially
Externí odkaz:
http://arxiv.org/abs/2102.05346
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