Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Result code in 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>

Alternative codes found

RIV/68407700:21730/25:00385896

Result on the web

<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>

Result language

angličtina

Original language name

Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2025

Confidentiality

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

Article name in the collection

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

ISBN

979-8-3315-1084-8

ISSN

2472-6737

e-ISSN

2642-9381

Number of pages

13

Pages from-to

7669-7681

Publisher name

IEEE

Place of publication

Piscataway

Event location

Tucson

Event date

Feb 28, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001521272600255