A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Indoor interior segmentation with curved surfaces via global energy optimization
Abstract Most existing indoor interior segmentation methods typically focus on planar structures rather than curved structures. Random sample consensus-based methods perform curved surface segmentation via regularity model fitting but suffer from spurious model generation when noise and outliers are present due to the uncertainty in the random sampling. This paper formulates indoor interior segmentation by fitting representative models and matching each primary cell with corresponding model simultaneously. The models are fitted by a combination of points and supervoxels with adaptive resolutions instead of just points, guaranteeing the correctness of sampling on the same surface and avoiding spurious models. Cell-to-model matching is achieved by iterative refinement/clustering under the global energy optimization method, which ensures optimal overall segmentations. Experimental tests demonstrate the effectiveness of our method in dealing with both planar and nonplanar surfaces, resulting in performance metrics of approximately 0.75 for the structure F1-score and over 0.9 for edge precision and recall.
Highlights Indoor interior segmentation is formulated by fitting representative models and cell-to-model matching simultaneously. The models are fitted by a combination of points and supervoxels with adaptive resolutions instead of just points. Cell-to-model matching is achieved by iterative refinement/clustering under the global energy optimization method.
Indoor interior segmentation with curved surfaces via global energy optimization
Abstract Most existing indoor interior segmentation methods typically focus on planar structures rather than curved structures. Random sample consensus-based methods perform curved surface segmentation via regularity model fitting but suffer from spurious model generation when noise and outliers are present due to the uncertainty in the random sampling. This paper formulates indoor interior segmentation by fitting representative models and matching each primary cell with corresponding model simultaneously. The models are fitted by a combination of points and supervoxels with adaptive resolutions instead of just points, guaranteeing the correctness of sampling on the same surface and avoiding spurious models. Cell-to-model matching is achieved by iterative refinement/clustering under the global energy optimization method, which ensures optimal overall segmentations. Experimental tests demonstrate the effectiveness of our method in dealing with both planar and nonplanar surfaces, resulting in performance metrics of approximately 0.75 for the structure F1-score and over 0.9 for edge precision and recall.
Highlights Indoor interior segmentation is formulated by fitting representative models and cell-to-model matching simultaneously. The models are fitted by a combination of points and supervoxels with adaptive resolutions instead of just points. Cell-to-model matching is achieved by iterative refinement/clustering under the global energy optimization method.
Indoor interior segmentation with curved surfaces via global energy optimization
Su, Fei (author) / Zhu, Haihong (author) / Li, Lin (author) / Zhou, Gang (author) / Rong, Wei (author) / Zuo, Xinkai (author) / Li, Wende (author) / Wu, Xinmei (author) / Wang, Weilin (author) / Yang, Fan (author)
2021-08-09
Article (Journal)
Electronic Resource
English
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