Name
Abstract | ||
|---|---|---|
We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13234930 | REMOTE SENSING |
Keywords | DocType | Volume |
interior structure, reconstruction, PCA, projection, ROR, thinning, nearest neighbor, slopes | Journal | 13 |
Issue | Citations | PageRank |
23 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongyang Cheng | 1 | 1 | 1.04 |
| Junchao Zhang | 2 | 1 | 3.41 |
| Dangjun Zhao | 3 | 1 | 1.04 |
| Jianlai Chen | 4 | 0 | 2.37 |
| Di Tian | 5 | 1 | 0.70 |