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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | No |
| Title | Automatic 3D Reconstruction of Structured Indoor Environments |
| Organization Unit | |
| Authors |
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| Presentation Type | other |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 10:1 - 218 |
| Event Title | ACM SIGGRAPH Courses |
| Event Type | conference |
| Event Location | Los Angeles |
| Event Start Date | August 17 - 2020 |
| Event End Date | August 28 - 2020 |
| Number | Article 10 |
| Place of Publication | Los Angeles |
| Publisher | ACM Digital Library |
| Abstract Text | Creating high-level structured 3D models of real-world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this tutorial, we provide an up-to-date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends. |
| Free access at | DOI |
| Related URLs | |
| Digital Object Identifier | 10.1145/3388769.3407469 |
| Other Identification Number | merlin-id:19827 |
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