Our proposal successfully exploits the knowledge included in previously trained CNN models to annotate 3D input images, that feed a semi-supervised 3D object ...
Semi-supervised 3D Object Recognition through CNN Labeling
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Specifically, we take advantage of the spatial information in the 3D data to segment objects in the image and build an object classifier, and the classification ...
A novel approach for 3D object recognition is proposed. •. The proposal relies on deep learning pre-trained models for image annotation. •.
Semi-supervised 3D object recognition through CNN labeling ; Elsevier · Applied Soft Computing. 2018, 65: 603-613. doi:10.1016/j.asoc.2018.02.005.
In this paper, we propose to merge techniques using both 2D and 3D information to overcome these problems. Specifically, we take advantage of ...
Jul 11, 2024 · This paper presents a comprehensive review of 27 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers.
Pseudo-Labeling (PL) is a critical approach in semi- supervised 3D object detection (SSOD). In PL, delicately selected pseudo-labels, generated by the ...
Missing: recognition | Show results with:recognition
Semi-Supervised 3D Object Recognition Through CNN Labeling by José Carlos Rangel, Jesus Martínez-Gómez, Cristina Romero-González, Ismael.
Specifically, we take advantage of the spatial information in the 3D data to segment objects in the image and build an object classifier, and the classification ...
Jul 11, 2024 · This paper presents a comprehensive review of 27 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers.
Missing: 3D | Show results with:3D