The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Scalable real-time volumetric surface reconstruction
Abstract
We address the fundamental challenge of scalability for real-time volumetric surface reconstruction methods. We design a memory efficient, hierarchical data structure for commodity graphics hardware, which supports live reconstruction of large-scale scenes with fine geometric details. Our sparse data structure fuses overlapping depth maps from a moving depth camera into a single volumetric representation, from which detailed surface models are extracted. Our hierarchy losslessly streams data bidirectionally between GPU and host, allowing for unbounded reconstructions. Our pipeline, comprised of depth map post-processing, camera pose estimation, volumetric fusion, surface extraction, and streaming, runs entirely in real-time. We experimentally demonstrate that a shallow hierarchy with relatively large branching factors yields the best memory/speed tradeoff, consuming an order of magnitude less memory than a regular grid. We compare an implementation of our data structure to existing methods and demonstrate higher-quality reconstructions on a variety of large-scale scenes, all captured in real-time.
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Cites
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.19 · Cited by 2,457
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078
Cited by
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368
ElasticFusion: Dense SLAM Without A Pose Graph
Whelan, T., Leutenegger, S., Salas Moreno, R., Glocker, B., & Davison, A. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.001 · Cited by 835
VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction
Innmann, M., Zollhöfer, M., Nießner, M., Theobalt, C., & Stamminger, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_22 · Cited by 308
Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
Wang, H., Wang, J., & Agapito, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01277 · Cited by 287
Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction
Vineet, V., Miksik, O., Lidegaard, M., Niebner, M., Golodetz, S., Prisacariu, V. A., Kahler, O., Murray, D. W., Izadi, S., Peerez, P., et al. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7138983 · Cited by 200
Self-citation
Metadata
- Authors
- Chen, J., Bautembach, D., & Izadi, S.
- Venue
- ACM Transactions on Graphics
- Published
- 2013-07-21
- Volume
- 32
- Issue
- 4
- Pages
- 1-16
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2071398263
- Cited by
- 247
- References
- 2
- FWCI
- 27.78
- Type
- journal article
- Language
- en
- Open access
- closed
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