DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation

• URL: http://arxiv.org/abs/2207.02805v1
• Date: Wed, 6 Jul 2022 16:48:56 GMT
• Title: DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation
• Authors: Ivan Shugurov, Sergey Zakharov, Slobodan Ilic
• Abstract summary: We propose a 3 stage 6 DoF object detection method called DPODv2 (Dense Pose Object Detector) We combine a 2D object detector with a dense correspondence estimation network and a multi-view pose refinement method to estimate a full 6 DoF pose. DPODv2 achieves excellent results on all of them while still remaining fast and scalable independent of the used data modality and the type of training data.
• Score: 24.770767430749288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a three-stage 6 DoF object detection method called DPODv2 (Dense Pose Object Detector) that relies on dense correspondences. We combine a 2D object detector with a dense correspondence estimation network and a multi-view pose refinement method to estimate a full 6 DoF pose. Unlike other deep learning methods that are typically restricted to monocular RGB images, we propose a unified deep learning network allowing different imaging modalities to be used (RGB or Depth). Moreover, we propose a novel pose refinement method, that is based on differentiable rendering. The main concept is to compare predicted and rendered correspondences in multiple views to obtain a pose which is consistent with predicted correspondences in all views. Our proposed method is evaluated rigorously on different data modalities and types of training data in a controlled setup. The main conclusions is that RGB excels in correspondence estimation, while depth contributes to the pose accuracy if good 3D-3D correspondences are available. Naturally, their combination achieves the overall best performance. We perform an extensive evaluation and an ablation study to analyze and validate the results on several challenging datasets. DPODv2 achieves excellent results on all of them while still remaining fast and scalable independent of the used data modality and the type of training data

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