Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00385896" target="_blank" >RIV/68407700:21230/25:00385896 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/25:00385896

Výsledek na webu

<a href="https://doi.org/10.1109/WACV61041.2025.00745" target="_blank" >https://doi.org/10.1109/WACV61041.2025.00745</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WACV61041.2025.00745" target="_blank" >10.1109/WACV61041.2025.00745</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Popis výsledku v původním jazyce

Current generalizable object pose estimators, i.e., approaches that do not need to be trained per object, rely on accurate 3D models. Predominantly, CAD models are used, which can be hard to obtain in practice. At the same time, it is often possible to acquire images of an object. Naturally, this leads to the question of whether 3D models reconstructed from images are sufficient to facilitate accurate object pose estimation. We aim to answer this question by proposing a novel benchmark for measuring the impact of 3D reconstruction quality on pose estimation accuracy. Our benchmark provides calibrated images suitable for reconstruction and registered with the test images of the YCB-V dataset for pose evaluation under the BOP benchmark format. Detailed experiments with multiple state-of-the-art 3D reconstruction and object pose estimation approaches show that the geometry produced by modern reconstruction methods is often sufficient for accurate pose estimation. Our experiments lead to interesting observations: (1) Standard metrics for measuring 3D reconstruction quality are not necessarily indicative of pose estimation accuracy, which shows the need for dedicated benchmarks such as ours. (2) Classical, non-learning-based approaches can perform on par with modern learning-based reconstruction techniques and can even offer a better reconstruction time-pose accuracy tradeoff. (3) There is still a sizable gap between performance with reconstructed and with CAD models. To foster research on closing this gap, the benchmark is made available at https://github.com/VarunBurde/reconstruction_pose_benchmark.

Název v anglickém jazyce

Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Popis výsledku anglicky

Current generalizable object pose estimators, i.e., approaches that do not need to be trained per object, rely on accurate 3D models. Predominantly, CAD models are used, which can be hard to obtain in practice. At the same time, it is often possible to acquire images of an object. Naturally, this leads to the question of whether 3D models reconstructed from images are sufficient to facilitate accurate object pose estimation. We aim to answer this question by proposing a novel benchmark for measuring the impact of 3D reconstruction quality on pose estimation accuracy. Our benchmark provides calibrated images suitable for reconstruction and registered with the test images of the YCB-V dataset for pose evaluation under the BOP benchmark format. Detailed experiments with multiple state-of-the-art 3D reconstruction and object pose estimation approaches show that the geometry produced by modern reconstruction methods is often sufficient for accurate pose estimation. Our experiments lead to interesting observations: (1) Standard metrics for measuring 3D reconstruction quality are not necessarily indicative of pose estimation accuracy, which shows the need for dedicated benchmarks such as ours. (2) Classical, non-learning-based approaches can perform on par with modern learning-based reconstruction techniques and can even offer a better reconstruction time-pose accuracy tradeoff. (3) There is still a sizable gap between performance with reconstructed and with CAD models. To foster research on closing this gap, the benchmark is made available at https://github.com/VarunBurde/reconstruction_pose_benchmark.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

ISBN

979-8-3315-1084-8

ISSN

2472-6737

e-ISSN

2642-9381

Počet stran výsledku

13

Strana od-do

7669-7681

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Tucson

Datum konání akce

28. 2. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001521272600255