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20182024
most citedLearning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning

26 citations · 35 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Human Pose Estimation in Top-View Fisheye Images

Jingrui Yu, Dipankar Nandi, Roman Seidel +1

Human pose estimation (HPE) in the top-view using fisheye cameras presents a promising and innovative application domain. However, the availability of datasets capturing this viewp…

cs.CV2023★ 1 cited

Human Pose Estimation in Monocular Omnidirectional Top-View Images

Jingrui Yu, Tobias Scheck, Roman Seidel +3

Human pose estimation (HPE) with convolutional neural networks (CNNs) for indoor monitoring is one of the major challenges in computer vision. In contrast to HPE in perspective vie…

cs.CV2022★ 8 cited

OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes

Jingrui Yu, Roman Seidel, Gangolf Hirtz

We propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competi…

cs.CV2021

OmniFlow: Human Omnidirectional Optical Flow

Roman Seidel, André Apitzsch, Gangolf Hirtz

Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end,…

cs.CV2020★ 26 cited

Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning

Tobias Scheck, Roman Seidel, Gangolf Hirtz

Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this…

cs.CV2018

Cubes3D: Neural Network based Optical Flow in Omnidirectional Image Scenes

André Apitzsch, Roman Seidel, Gangolf Hirtz

Optical flow estimation with convolutional neural networks (CNNs) has recently solved various tasks of computer vision successfully. In this paper we adapt a state-of-the-art appro…