2022
DOI: 10.1109/lra.2022.3146914
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Improving Depth Estimation Using Map-Based Depth Priors

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Cited by 4 publications
(1 citation statement)
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“…Using prior maps to improve perception during deployment has been explored before, as demonstrated on depth estimation [38], semantic segmentation [39], 3D object detection [40]- [42], visual odometry [43], motion planning [44], [45], ego-lane detection [46] and change detection for road infrastructure either using 3D maps [47]- [49] or through image-to-image comparison [50], [51]. Despite the rapidly growing literature, existing approaches have limitations: they either rely on 3D maps (with significant variations in what map elements are used) [38], [40]- [42], [52]; only focus on updating static parts of the map [46]- [49]; require information from multiple prior traverses at test time [52]; or require pixel-level correspondences when using imageto-image setting [39], [43] In this work, for the task of dynamic vehicle detection in 2D, we consider a repeated route scenario for an autonomous vehicle. Thus, without needing a 3D map or pixel-level correspondences (especially under adverse conditions), we show how dynamic vehicle detections in the query image can be validated by comparing corresponding regions in the query and the reference image obtained through VPR.…”
Section: Prior Maps For Perceptionmentioning
confidence: 99%
“…Using prior maps to improve perception during deployment has been explored before, as demonstrated on depth estimation [38], semantic segmentation [39], 3D object detection [40]- [42], visual odometry [43], motion planning [44], [45], ego-lane detection [46] and change detection for road infrastructure either using 3D maps [47]- [49] or through image-to-image comparison [50], [51]. Despite the rapidly growing literature, existing approaches have limitations: they either rely on 3D maps (with significant variations in what map elements are used) [38], [40]- [42], [52]; only focus on updating static parts of the map [46]- [49]; require information from multiple prior traverses at test time [52]; or require pixel-level correspondences when using imageto-image setting [39], [43] In this work, for the task of dynamic vehicle detection in 2D, we consider a repeated route scenario for an autonomous vehicle. Thus, without needing a 3D map or pixel-level correspondences (especially under adverse conditions), we show how dynamic vehicle detections in the query image can be validated by comparing corresponding regions in the query and the reference image obtained through VPR.…”
Section: Prior Maps For Perceptionmentioning
confidence: 99%