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VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization

CVPR 2017 · Video › Video segmentation and tracking · approach: Layered video decomposition and dynamics · 251 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a recurrent video-clip model instead of independent single-frame pose predictionVidLoc · 7-Sceneslocalization accuracy: 23.4% - 55% increase in accuracyHelped
Use VidLoc (RGB) instead of Posenet7-Scenes Office · 7-Scenes Officetranslation error: 0.48m -> 0.26mHelped
Use VidLoc (RGB) instead of Posenet7-Scenes Stairs · 7-Scenes Stairstranslation error: 0.47m -> 0.26mHelped
Use VidLoc (RGB) instead of Score-Forest7-Scenes · 7-Scenestranslation errorMixed
Increase the video sequence/window lengthVidLoc · 7-Sceneslocalization errorHelped
Increase the video sequence lengthVidLoc · Oxford RobotCarlocalization accuracyHelped
Use sequence length 50 or 100 rather than a single imageVidLoc · Oxford RobotCartrajectory localization: 50 and 100Helped
Use VidLoc instead of PosenetOxford RobotCar, sequence length 100 · Oxford RobotCarposes within 20 meters: more than half; less than 15%Helped
Use RGB-D input instead of RGB-only inputVidLoc · 7-Scenestranslation errorHelped
Use VidLoc with RGB-D rather than Score-Forest7-Scenes · 7-Scenestranslation errorHurt

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