What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector

• URL: http://arxiv.org/abs/2007.14812v2
• Date: Wed, 24 Mar 2021 18:11:37 GMT
• Title: What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector
• Authors: C\'edric Picron, Punarjay Chakravarty, Tom Roussel, Tinne Tuytelaars
• Abstract summary: 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. We subsequently demonstrate an optimization-based monocular 3D bounding box detector built on top of the self-supervised vehicle orientation estimator.
• Score: 41.12124329933595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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