3D Key-Points Estimation from Single-View RGB Images

Zohaib, Mohammad; Taiana, Matteo; Padalkar, Milind G.; Bue, Alessio Del

doi:10.1007/978-3-031-06430-2_3

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Several methods have been proposed to estimate 3D keypoints in a supervised way using human-annotated keypoints [32,6,40,12,44,11,36]. As our approach is unsupervised, here we review in more detail the methods that do not use supervision.…”

Section: Related Workmentioning

confidence: 99%

“…The solution to this problem was initially cast as a supervised learning task: given a dataset of manually annotated PCDs with keypoints, a computational model infers the keypoints position given a PCD as input [32,44,13,8,36]. While these methods provided impressive results on the dataset they were trained on, they also highlighted the limitations of supervised approaches.…”

Section: Introductionmentioning

confidence: 99%

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Cdhn: Cross-Domain Hallucination Network for 3d Keypoints Estimation

Zohaib

Padalkar

Morerio

et al. 2023

Preprint

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cdhn: Cross-Domain Hallucination Network for 3d Keypoints Estimation

Zohaib

Padalkar

Morerio

et al. 2023

Preprint

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“…Several object detection frameworks have been reported with the advent of DL-based frameworks, including onestage detectors [25], [26], as well as two-stage detectors [27] [28]. Mostly, DL-based frameworks use pre-trained models to improve and refine the feature extraction, such as in [29]. In [30], a review of some of the main state-ofthe-art object detection frameworks is provided along with some experimental analysis.…”

Section: Background and Motivationmentioning

confidence: 99%

A Systematic Review of Drone Based Road Traffic Monitoring System

et al. 2022

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Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities INDEX TERMS deep learning, drone, smart city, traffic monitoring, uav.

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SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data

Zohaib,

Del Bue

2023

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

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3D Key-Points Estimation from Single-View RGB Images

Cited by 5 publications

References 25 publications

Cdhn: Cross-Domain Hallucination Network for 3d Keypoints Estimation

Cdhn: Cross-Domain Hallucination Network for 3d Keypoints Estimation

A Systematic Review of Drone Based Road Traffic Monitoring System

SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data

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