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Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
Open accessAbstract
We present a new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs.Our modeling approach, which combines both geometry-based and imagebased techniques, has two components.The first component is a photogrammetric modeling method which facilitates the recovery of the basic geometry of the photographed scene.Our photogrammetric modeling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes.The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model.By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs.Consequently, our approach can model large architectural environments with far fewer photographs than current image-based modeling approaches.For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models.Our approach can be used to recover models for use in either geometry-based or image-based rendering systems.We present results that demonstrate our approach's ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs.
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Cited by
NeRF: representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2022) · Communications of the ACM · doi:10.1145/3503250 · Cited by 6,852
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_24 · Cited by 5,351
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.700 · Cited by 2,910
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., & Srinivasan, P. P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00580 · Cited by 1,921
RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911434148 · Cited by 1,161
Metadata
- Authors
- Debevec, P. E., Taylor, C. J., & Malik, J.
- Venue
- Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
- Publisher
- ACM
- Published
- 1996-08
- Volume
- 96
- Pages
- 11-20
- OpenAlex
- W1573748084
- Cited by
- 1,905
- References
- 0
- FWCI
- 161.09
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Debevec, P. E., Taylor, C. J., & Malik, J. (1996). Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach. Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. https://doi.org/10.1145/237170.237191
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