MonoSOWA: Scalable Monocular 3D Object Detector Without Human Annotations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00386446" target="_blank" >RIV/68407700:21230/25:00386446 - isvavai.cz</a>

Výsledek na webu

<a href="https://openaccess.thecvf.com/content/ICCV2025/html/Skvrna_MonoSOWA_Scalable_Monocular_3D_Object_Detector_Without_Human_Annotations_ICCV_2025_paper.html" target="_blank" >https://openaccess.thecvf.com/content/ICCV2025/html/Skvrna_MonoSOWA_Scalable_Monocular_3D_Object_Detector_Without_Human_Annotations_ICCV_2025_paper.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

MonoSOWA: Scalable Monocular 3D Object Detector Without Human Annotations

Popis výsledku v původním jazyce

Inferring object 3D position and orientation from a single RGB camera is a foundational task in computer vision with many important applications. Traditionally, 3D object detection methods are trained in a fully-supervised setup, requiring LiDAR and vast amounts of human annotations, which are laborious, costly, and do not scale well with the ever-increasing amounts of data being captured.We present a novel method to train a 3D object detector from a single RGB camera without domain-specific human annotations, making orders of magnitude more data available for training. The method uses newly proposed Local Object Motion Model to disentangle object movement source between subsequent frames, is approximately 700 times faster than previous work and compensates camera focal length differences to aggregate multiple datasets.The method is evaluated on three public datasets, where despite using no human labels, it outperforms prior work by a significant margin. It also shows its versatility as a pre-training tool for fully-supervised training and shows that combining pseudo-labels from multiple datasets can achieve comparable accuracy to using human labels from a single dataset.

Název v anglickém jazyce

MonoSOWA: Scalable Monocular 3D Object Detector Without Human Annotations

Popis výsledku anglicky

Inferring object 3D position and orientation from a single RGB camera is a foundational task in computer vision with many important applications. Traditionally, 3D object detection methods are trained in a fully-supervised setup, requiring LiDAR and vast amounts of human annotations, which are laborious, costly, and do not scale well with the ever-increasing amounts of data being captured.We present a novel method to train a 3D object detector from a single RGB camera without domain-specific human annotations, making orders of magnitude more data available for training. The method uses newly proposed Local Object Motion Model to disentangle object movement source between subsequent frames, is approximately 700 times faster than previous work and compensates camera focal length differences to aggregate multiple datasets.The method is evaluated on three public datasets, where despite using no human labels, it outperforms prior work by a significant margin. It also shows its versatility as a pre-training tool for fully-supervised training and shows that combining pseudo-labels from multiple datasets can achieve comparable accuracy to using human labels from a single dataset.

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

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

ICCV2025: Proceedings of the International Conference on Computer Vision

ISBN

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ISSN

1550-5499

e-ISSN

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Počet stran výsledku

11

Strana od-do

7613-7623

Název nakladatele

IEEE Communications Society

Místo vydání

Anchorage

Místo konání akce

Honolulu

Datum konání akce

19. 10. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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