A Convolutional Network for Semantic Facade Segmentation and Interpretation

Schmitz, Matthias; Mayer, Helmut

doi:10.5194/isprsarchives-xli-b3-709-2016

Cited by 21 publications

(7 citation statements)

References 13 publications

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“…In recent years, most work on facade segmentation is based on single images (Cohen et al, 2014, Jampani et al, 2015, Mathias et al, 2016, Rahmani et al, 2017, Schmitz and Mayer, 2016. Additionally, the Varcity dataset (Riemenschneider et al, 2014) has been published focusing on facade image and facade point cloud labeling.…”

Section: Introductionmentioning

confidence: 99%

High Quality Facade Segmentation Based on Structured Random Forest, Region Proposal Network and Rectangular Fitting

Rahmani

Mayer

2018

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

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ABSTRACT:In this paper we present a pipeline for high quality semantic segmentation of building facades using Structured Random Forest (SRF), Region Proposal Network (RPN) based on a Convolutional Neural Network (CNN) as well as rectangular fitting optimization. Our main contribution is that we employ features created by the RPN as channels in the SRF. We empirically show that this is very effective especially for doors and windows. Our pipeline is evaluated on two datasets where we outperform current state-of-the-art methods. Additionally, we quantify the contribution of the RPN and the rectangular fitting optimization on the accuracy of the result.

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Section: Introductionmentioning

confidence: 99%

High Quality Facade Segmentation Based on Structured Random Forest, Region Proposal Network and Rectangular Fitting

Rahmani

Mayer

2018

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

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“…We are not able to detect such windows based on relative depth information, so we need a further analysis, e.g., of the rectified façade image. This can be done by employing the grammar based approaches (Teboul et al, 2013, Martinovic andVan Gool, 2014) or by façade image interpretation, e.g., by convolutional networks (Schmitz and Mayer, 2016) or by a marked point process (Wenzel and Förstner, 2016). …”

Section: Discussionmentioning

confidence: 99%

Automatic Generation of Building Models With Levels of Detail 1-3

Nguatem¹,

Drauschke²,

Mayer³

2016

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

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ABSTRACT:We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing , we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrate our workflow up to the export into CityGML.

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“…The use of machine learning techniques on images for per pixel predictions is discussed in (Long et al, 2017). (Schmitz and Mayer, 2016) use an end-to-end learned convolutional Neural Network (CNN) for image segmentation on facades. This fully convolutional approach was extended with optional man-made rules concerning the typical symmetry found in structures reporting segmentation accuracies for windows and doors of 93.04% and 90.95% respectively on the S3DIS dataset .…”

Section: Window and Door Detection And Modellingmentioning

confidence: 99%

Review of Window and Door Type Detection Approaches

Geyter

Bassier²,

Winter³

et al. 2022

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

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Abstract. The use of as-built Building Information Models (BIM) has become increasingly commonplace. This process of creating a BIM model from point cloud data, also referred to as Scan-to-BIM, is a mostly manual task. Due to the large amount of manual work, the entire Scan-to-BIM process is time-consuming and error prone. Current research focuses on the automation of the Scan-to-BIM pipeline by applying state-of-the-art techniques on its consecutive steps including the data acquisition, data processing, data interpretation and modelling. By automating the matching and modelling of window and door objects, a considerable amount of time can be saved in the Scan-to-BIM process. This is so because each window and door instance needs to be examined by the modeller and must be adapted to the actual on-site situation. Large object libraries containing predefined window and door objects exists but the matching to the best-fit predefined object remains time consuming. The aim of this research is to examine the possibilities to speed up the modelling of window and door objects. First, a literature review discussing existing methods for window and door detection and matching is presented. Second, the acquired data is examined to explore the capabilities of capturing window and door information for different remote sensing devices. Followed by tests of some commonplace features in the use for window and door occurrence matching and clustering.

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A Convolutional Network for Semantic Facade Segmentation and Interpretation

Cited by 21 publications

References 13 publications

High Quality Facade Segmentation Based on Structured Random Forest, Region Proposal Network and Rectangular Fitting

High Quality Facade Segmentation Based on Structured Random Forest, Region Proposal Network and Rectangular Fitting

Automatic Generation of Building Models With Levels of Detail 1-3

Review of Window and Door Type Detection Approaches

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