BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00371804" target="_blank" >RIV/68407700:21230/23:00371804 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/CVPRW59228.2023.00279" target="_blank" >https://doi.org/10.1109/CVPRW59228.2023.00279</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPRW59228.2023.00279" target="_blank" >10.1109/CVPRW59228.2023.00279</a>

Jazyk výsledku

angličtina

Název v původním jazyce

BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects

Popis výsledku v původním jazyce

We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB/RGB-D image. In 2022, we witnessed another significant improvement in the pose estimation accuracy – the state of the art, which was 56.9 AR C in 2019 (Vidal et al.) and 69.8 AR C in 2020 (CosyPose), moved to new heights of 83.7 AR C (GDRNPP). Out of 49 pose estimation methods evaluated since 2019, the top 18 are from 2022. Methods based on point pair features, which were introduced in 2010 and achieved competitive results even in 2020, are now clearly outperformed by deep learning methods. The synthetic-to-real domain gap was again significantly reduced, with 82.7 AR C achieved by GDRNPP trained only on synthetic images from BlenderProc. The fastest variant of GDRNPP reached 80.5 AR C with an average time per image of 0.23s. Since most of the recent methods for 6D object pose estimation begin by detecting/segmenting objects, we also started evaluating 2D object detection and segmentation performance based on the COCO metrics. Compared to the Mask R-CNN results from CosyPose in 2020, detection improved from 60.3 to 77.3 AP C and segmentation from 40.5 to 58.7 AP C . The online evaluation system stays open and is available at: bop.felk.cvut.cz.

Název v anglickém jazyce

BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects

Popis výsledku anglicky

We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB/RGB-D image. In 2022, we witnessed another significant improvement in the pose estimation accuracy – the state of the art, which was 56.9 AR C in 2019 (Vidal et al.) and 69.8 AR C in 2020 (CosyPose), moved to new heights of 83.7 AR C (GDRNPP). Out of 49 pose estimation methods evaluated since 2019, the top 18 are from 2022. Methods based on point pair features, which were introduced in 2010 and achieved competitive results even in 2020, are now clearly outperformed by deep learning methods. The synthetic-to-real domain gap was again significantly reduced, with 82.7 AR C achieved by GDRNPP trained only on synthetic images from BlenderProc. The fastest variant of GDRNPP reached 80.5 AR C with an average time per image of 0.23s. Since most of the recent methods for 6D object pose estimation begin by detecting/segmenting objects, we also started evaluating 2D object detection and segmentation performance based on the COCO metrics. Compared to the Mask R-CNN results from CosyPose in 2020, detection improved from 60.3 to 77.3 AP C and segmentation from 40.5 to 58.7 AP C . The online evaluation system stays open and is available at: bop.felk.cvut.cz.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Whorkshops (CVPRW)

ISBN

979-8-3503-0250-9

ISSN

2160-7508

e-ISSN

2160-7516

Počet stran výsledku

10

Strana od-do

2785-2794

Název nakladatele

IEEE Computer Society

Místo vydání

USA

Místo konání akce

Vancouver

Datum konání akce

18. 6. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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