M
Matthew Grimes
Publications - 6
Citations - 2353
Matthew Grimes is an academic researcher. The author has contributed to research in topics: Motion blur & Intrinsics. The author has an hindex of 5, co-authored 5 publications receiving 1684 citations.
Papers
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Proceedings ArticleDOI
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
TL;DR: PoseNet as mentioned in this paper uses a CNN to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation.
Posted Content
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
TL;DR: This work trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation, demonstrating that convnets can be used to solve complicated out of image plane regression problems.
Posted Content
Convolutional networks for real-time 6-DOF camera relocalization.
TL;DR: This work trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation, demonstrating that convnets can be used to solve complicated out of image plane regression problems.
Dataset
Research data supporting “PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization”: St Marys Church
TL;DR: St Mary's Church scene from Cambridge Landmarks, a large scale outdoor visual relocalisation dataset taken around Cambridge University, contains original video, with extracted image frames labelled with their 6-DOF camera pose and a visual reconstruction of the scene.
Dataset
Research data supporting “PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization”: Kings College
TL;DR: King's College scene from Cambridge Landmarks, a large scale outdoor visual relocalisation dataset taken around Cambridge University, contains original video, with extracted image frames labelled with their 6-DOF camera pose and a visual reconstruction of the scene.