Online facade reconstruction from dominant frequencies in structured point clouds

Friedman, Samuel; Stamos, Ioannis

doi:10.1109/cvprw.2012.6238908

Cited by 20 publications

(11 citation statements)

References 16 publications

(15 reference statements)

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“…As mentioned in the introduction, most authors opt for automatic occlusion correction without focusing on what produced the occlusion. In papers where there is a detection phase, detection and correction is usually based on visibility analysis (Friedman and Stamos, 2012), for which it is necessary to know the exact 3D TLS position or MLS trajectory. In this work, trajectory has been not necessary as input data, since many times this data is not available.…”

Section: Discussionmentioning

confidence: 99%

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Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Balado

González

Verbree

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.

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Section: Discussionmentioning

confidence: 99%

“…Occlusions are one of the main limitations of point clouds (Friedman and Stamos, 2012). Occlusions imply a lack of data and therefore a lack of knowledge about empty areas.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Balado

González

Verbree

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Friedman and Stamos (2012) introduced a method to fill the gaps in the point clouds which are caused by occluding objects. The proposed approach fills the point cloud gaps successfully if the building facades have regular repetitive patterns.…”

Section: Related Literaturementioning

confidence: 99%

Quality Assessment and Comparison of Smartphone and Leica C10 Laser Scanner Based Point Clouds

Sırmaçek¹,

Lindenbergh²,

Wang³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (which can generate point clouds based on photogrammetry) can provide denser sampling of 3D surface geometry. Unfortunately, terrestrial laser scanning sensors are expensive and trained persons are needed to use them for point cloud acquisition. A potential effective 3D modelling can be generated based on a low cost smartphone sensor. Herein, we show examples of using smartphone camera images to generate 3D models of urban structures. We compare a smartphone based 3D model of an example structure with a terrestrial laser scanning point cloud of the structure. This comparison gives us opportunity to discuss the differences in terms of geometrical correctness, as well as the advantages, disadvantages and limitations in data acquisition and processing. We also discuss how smartphone based point clouds can help to solve further problems with 3D urban model generation in a practical way. We show that terrestrial laser scanning point clouds which do not have color information can be colored using smartphones. The experiments, discussions and scientific findings might be insightful for the future studies in fast, easy and low-cost 3D urban model generation field.

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“…There is a large amount of work on urban building and facade modeling, ranging from ruled‐based [MWH*06], image‐based [FCSS09] and 3D point‐based [FS12] modeling approaches. A recent survey comprehensively introduces the related work to urban space modeling [MWA*12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modeling of Urban Facades from Raw LiDAR Point Data

Wang

Remil

et al. 2016

Computer Graphics Forum

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Figure 1: A large-scale urban architectural scene is automatically and efficiently reconstructed in details from a noisy and sparse LiDAR scan using our method without user interactions. Zoom-in views show the reconstructed details of 3D repetitive structures. There are totally 478, 172, 981 points within the scans and the reconstruction time is less than one hour. AbstractModeling of urban facades from raw LiDAR point data remains active due to its challenging nature. In this paper, we propose an automatic yet robust 3D modeling approach for urban facades with raw LiDAR point clouds. The key observation is that building facades often exhibit repetitions and regularities. We hereby formulate repetition detection as an energy optimization problem with a global energy function balancing geometric errors, regularity and complexity of facade structures. As a result, repetitive structures are extracted robustly even in the presence of noise and missing data. By registering repetitive structures, missing regions are completed and thus the associated point data of structures are well consolidated. Subsequently, we detect the potential design intents (i.e., geometric constraints) within structures and perform constrained fitting to obtain the precise structure models. Furthermore, we apply structure alignment optimization to enforce position regularities and employ repetitions to infer missing structures. We demonstrate how the quality of raw LiDAR data can be improved by exploiting data redundancy, and discovering high level structural information (regularity and symmetry). We evaluate our modeling method on a variety of raw LiDAR scans to verify its robustness and effectiveness. J. Wang et al. / Automatic Modeling of Urban Facades from Raw LiDAR Point Dataand constraints of facade structures directly over raw LiDAR point cloud data. With different scales, the urban facades usually present a high degree of symmetry and their attached structures (e.g., windows) are often regularly repeated. Based on this observation, our facade modeling method proceeds as repetitive structure detection, fitting and locating.

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Online facade reconstruction from dominant frequencies in structured point clouds

Cited by 20 publications

References 16 publications

Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Quality Assessment and Comparison of Smartphone and Leica C10 Laser Scanner Based Point Clouds

Automatic Modeling of Urban Facades from Raw LiDAR Point Data

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