Name
Title | ||
|---|---|---|
Topologically Consistent Reconstruction for Complex Indoor Structures from Point Clouds |
Abstract | ||
|---|---|---|
Indoor structures are composed of ceilings, walls and floors that need to be modeled for a variety of applications. This paper proposes an approach to reconstructing models of indoor structures in complex environments. First, semantic pre-processing, including segmentation and occlusion construction, is applied to segment the input point clouds to generate semantic patches of structural primitives with uniform density. Then, a primitives extraction method with detected boundary is introduced to approximate both the mathematical surface and the boundary of the patches. Finally, a constraint-based model reconstruction is applied to achieve the final topologically consistent structural model. Under this framework, both the geometric and structural constraints are considered in a holistic manner to assure topologic regularity. Experiments were carried out with both synthetic and real-world datasets. The accuracy of the proposed method achieved an overall reconstruction quality of approximately 4.60 cm of root mean square error (RMSE) and 94.10% Intersection over Union (IoU) of the input point cloud. The development can be applied for structural reconstruction of various complex indoor environments. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13193844 | REMOTE SENSING |
Keywords | DocType | Volume |
indoor mapping, 3D modeling, reconstruction, point clouds, topological consistency | Journal | 13 |
Issue | Citations | PageRank |
19 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengchi Ai | 1 | 0 | 0.68 |
| Zhixin Li | 2 | 111 | 24.43 |
| J. Shan | 3 | 220 | 20.08 |