FocalPose++: Focal Length and Object Pose Estimation via Render and Compare
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00380610" target="_blank" >RIV/68407700:21230/25:00380610 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/25:00380610
Result on the web
<a href="https://doi.org/10.1109/TPAMI.2024.3475638" target="_blank" >https://doi.org/10.1109/TPAMI.2024.3475638</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2024.3475638" target="_blank" >10.1109/TPAMI.2024.3475638</a>
Alternative languages
Result language
angličtina
Original language name
FocalPose++: Focal Length and Object Pose Estimation via Render and Compare
Original language description
We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. Third, we explore the effect of different synthetic training data on the performance of our method. Specifically, we investigate different distributions used for sampling object's 6D pose and camera's focal length when rendering the synthetic images, and show that parametric distribution fitted on real training data works the best. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
1939-3539
Volume of the periodical
47
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
755-772
UT code for WoS article
001395340500016
EID of the result in the Scopus database
2-s2.0-85207033812