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- W4297788802 abstract "The estimation of the orientation of an observed vehicle relative to an Autonomous Vehicle (AV) from monocular camera data is an important building block in estimating its 6 DoF pose. Current Deep Learning based solutions for placing a 3D bounding box around this observed vehicle are data hungry and do not generalize well. In this paper, we demonstrate the use of monocular visual odometry for the self-supervised fine-tuning of a model for orientation estimation pre-trained on a reference domain. Specifically, while transitioning from a virtual dataset (vKITTI) to nuScenes, we recover up to 70% of the performance of a fully supervised method. We subsequently demonstrate an optimization-based monocular 3D bounding box detector built on top of the self-supervised vehicle orientation estimator without the requirement of expensive labeled data. This allows 3D vehicle detection algorithms to be self-trained from large amounts of monocular camera data from existing commercial vehicle fleets." @default.
- W4297788802 created "2022-10-01" @default.
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- W4297788802 date "2020-07-29" @default.
- W4297788802 modified "2023-10-18" @default.
- W4297788802 title "What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector" @default.
- W4297788802 doi "https://doi.org/10.48550/arxiv.2007.14812" @default.
- W4297788802 hasPublicationYear "2020" @default.
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