MonoSOWA: Scalable Monocular 3D Object Detector Without Human Annotations

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

MonoSOWA: Scalable Monocular 3D Object Detector Without Human Annotations

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

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)

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

ICCV2025: Proceedings of the International Conference on Computer Vision

ISBN

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ISSN

1550-5499

e-ISSN

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Number of pages

11

Pages from-to

7613-7623

Publisher name

IEEE Communications Society

Place of publication

Anchorage

Event location

Honolulu

Event date

Oct 19, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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