Benchmarking Open-Source Segmentation and Metric Monocular Depth Methods in 6DoF Object Pose Estimation with FoundationPose
- DOI
- 10.1145/3831600.3831602
- Published
- 2026-06-18
- Container
- Proceedings of the Intelligent Robotics FAIR 2026
- Publisher
- ACM
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1145/3831600.3831602,
title = {Benchmarking Open-Source Segmentation and Metric Monocular Depth Methods in 6DoF Object Pose Estimation with FoundationPose},
author = {Mickyas Tamiru Asfaw and Victor-Daniel Montana Cuellar and Nicolas Ragot and Fabrice Duval},
year = {2026},
journal = {Proceedings of the Intelligent Robotics FAIR 2026},
doi = {10.1145/3831600.3831602},
url = {https://doi.org/10.1145/3831600.3831602}
}RIS
TY - JOUR TI - Benchmarking Open-Source Segmentation and Metric Monocular Depth Methods in 6DoF Object Pose Estimation with FoundationPose AU - Mickyas Tamiru Asfaw AU - Victor-Daniel Montana Cuellar AU - Nicolas Ragot AU - Fabrice Duval PY - 2026 JO - Proceedings of the Intelligent Robotics FAIR 2026 DO - 10.1145/3831600.3831602 UR - https://doi.org/10.1145/3831600.3831602 ER -
APA
Asfaw, M. T., Cuellar, V. M., Ragot, N., & Duval, F. (2026). Benchmarking Open-Source Segmentation and Metric Monocular Depth Methods in 6DoF Object Pose Estimation with FoundationPose. Proceedings of the Intelligent Robotics FAIR 2026. https://doi.org/10.1145/3831600.3831602
Source records
- crossref · retrieved 2026-11-10T12:09:55.727Z