The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
State of the Art on 3D Reconstruction with RGB‐D Cameras
Abstract
The advent of affordable consumer grade RGB‐D cameras has brought about a profound advancement of visual scene reconstruction methods. Both computer graphics and computer vision researchers spend significant effort to develop entirely new algorithms to capture comprehensive shape models of static and dynamic scenes with RGB‐D cameras. This led to significant advances of the state of the art along several dimensions. Some methods achieve very high reconstruction detail, despite limited sensor resolution. Others even achieve real‐time performance, yet possibly at lower quality. New concepts were developed to capture scenes at larger spatial and temporal extent. Other recent algorithms flank shape reconstruction with concurrent material and lighting estimation, even in general scenes and unconstrained conditions. In this state‐of‐the‐art report, we analyze these recent developments in RGB‐D scene reconstruction in detail and review essential related work. We explain, compare, and critically analyze the common underlying algorithmic concepts that enabled these recent advancements. Furthermore, we show how algorithms are designed to best exploit the benefits of RGB‐D data while suppressing their often non‐trivial data distortions. In addition, this report identifies and discusses important open research questions and suggests relevant directions for future work.
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Cites
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Tretschk, E., Tewari, A., Golyanik, V., Zollhofer, M., Lassner, C., & Theobalt, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01272 · Cited by 432
Self-citationA LLM-informed multi-agent AI system for drone-based visual inspection for infrastructure
Liu, J., Li, H., Chai, C., Chen, K., & Wang, D. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103643 · Cited by 32
Plan2Scene: Converting Floorplans to 3D Scenes
Vidanapathirana, M., Wu, Q., Furukawa, Y., Chang, A. X., & Savva, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01059 · Cited by 32
Metadata
- Authors
- Zollhöfer, M., Stotko, P., Görlitz, A., Theobalt, C., Nießner, M., Klein, R., & Kolb, A.
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2018-05
- Volume
- 37
- Issue
- 2
- Pages
- 625-652
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W2805202215
- Cited by
- 339
- References
- 17
- FWCI
- 27.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zollhöfer, M., Stotko, P., Görlitz, A., Theobalt, C., Nießner, M., Klein, R., & Kolb, A. (2018). State of the Art on 3D Reconstruction with RGB‐D Cameras. Computer Graphics Forum. https://doi.org/10.1111/cgf.13386
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