E2ETag: An End-to-End Trainable Method for Generating and Detecting Fiducial Markers

29 May 2021

Papers citing "E2ETag: An End-to-End Trainable Method for Generating and Detecting Fiducial Markers"

7 / 7 papers shown

Title
Uncovering the Metaverse within Everyday Environments: a Coarse-to-Fine Approach Liming Xu Dave Towey Andrew P. French Steve Benford 25 1 0 11 Apr 2024
CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants Shaoan Wang Mingzhu Zhu Yaoqing Hu Dongyue Li Fusong Yuan Junzhi Yu 11 4 0 20 Oct 2023
Connecting Everyday Objects with the Metaverse: A Unified Recognition Framework Liming Xu Dave Towey Andrew P. French Steve Benford 26 3 0 11 Sep 2023
Neural Lens Modeling Wenqi Xian Aljavz Bovzivc Noah Snavely Christoph Lassner 3DV 32 7 0 10 Apr 2023
NeuralMarker: A Framework for Learning General Marker Correspondence Zhaoyang Huang Xiao Pan Weihong Pan Weikang Bian Yan Xu Ka Chun Cheung Guofeng Zhang Hongsheng Li 38 5 0 19 Sep 2022
DeepFormableTag: End-to-end Generation and Recognition of Deformable Fiducial Markers Mustafa B. Yaldiz Andreas Meuleman Hyeonjoong Jang Hyunho Ha Min H. Kim 3DV 22 7 0 16 Jun 2022
DeepTag: A General Framework for Fiducial Marker Design and Detection Zhuming Zhang Yongtao Hu Guoxing Yu Jingwen Dai 39 22 0 28 May 2021