User-Assisted Inverse Procedural Facade Modeling and Compressed Image Rendering

Zhuo, Huilong; Zhou, Shengchuan; Beneš, Bedřich; Whittinghill, David

doi:10.1007/978-3-319-27863-6_12

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Structure from motion provides a rough shape that is proceduralized into rectilinear patches that are further organized into a regular procedural model. In Wu et al [2014] a split grammar is generated by using dynamic programming for an input façade, and in Zhuo et al [2015] human intuition is used in a semiinteractive approach for inverse procedural modeling of façades. Vanegas et al [2010] use an L-system that incorporates only a single universal rule for the 3D reconstruction of Manhattan-style buildings.…”

Section: Inverse Procedural Modelingmentioning

confidence: 99%

Inverse Procedural Modeling of Branching Structures by Inferring L-Systems

et al. 2020

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We introduce an inverse procedural modeling approach that learns L-system representations of pixel images with branching structures. Our fully automatic model generates a compact set of textual rewriting rules that describe the input. We use deep learning to discover atomic structures such as line segments or branchings. Orientation and scaling of these structures are determined and the detected structures are combined into a tree. The initial representation is analyzed, and repeating parts are encoded into a small grammar by using greedy optimization while the user can control the size of the detected rules. The output is an L-system that represents the input image as a simple text and a set of terminal symbols. We apply our approach to a variety of examples, demonstrate its robustness against noise and blur, and we show that it can detect user sketches and complex input structures.

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Section: Inverse Procedural Modelingmentioning

confidence: 99%

Inverse Procedural Modeling of Branching Structures by Inferring L-Systems

et al. 2020

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“…The framework is usually effective and direct. Other studies [24][25][26][27][28][29][30][31][32][33][34][35][36][37] extend the field to semantic interpretation, facade modeling, etc. Our window detector works as a basic system for downstream applications.…”

Section: Facade Parsingmentioning

confidence: 99%

Window Detection in Facades Using Heatmap Fusion

Liu

Zhang

et al. 2020

J. Comput. Sci. Technol.

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Window detection is a key component in many graphics and vision applications related to 3D city modeling and scene visualization. We present a novel approach for learning to recognize windows in a colored facade image. Rather than predicting bounding boxes or performing facade segmentation, our system locates keypoints of windows, and learns keypoint relationships to group them together into windows. A further module provides extra recognizable information at the window center. Locations and relationships of keypoints are encoded in different types of heatmaps, which are learned in an end-to-end network. We have also constructed a facade dataset with 3 418 annotated images to facilitate research in this field. It has richly varying facade structures, occlusion, lighting conditions, and angle of view. On our dataset, our method achieves precision of 91.4% and recall of 91.0% under 50% IoU (intersection over union). We also make a quantitative comparison with state-of-the-art methods to verify the utility of our proposed method. Applications based on our window detector are also demonstrated, such as window blending.

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User-Assisted Inverse Procedural Facade Modeling and Compressed Image Rendering

Cited by 2 publications

References 23 publications

Inverse Procedural Modeling of Branching Structures by Inferring L-Systems

Inverse Procedural Modeling of Branching Structures by Inferring L-Systems

Window Detection in Facades Using Heatmap Fusion

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