Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation

• URL: http://arxiv.org/abs/2308.10016v1
• Date: Sat, 19 Aug 2023 13:52:18 GMT
• Title: Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation
• Authors: Yang Hai, Rui Song, Jiaojiao Li, David Ferstl, Yinlin Hu
• Abstract summary: We propose a 6D object pose estimation method that can be trained with pure RGB images without any auxiliary information. We evaluate our method on three challenging datasets and demonstrate that it outperforms state-of-the-art self-supervised methods significantly.
• Score: 14.469317161361202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their application range. In this paper, we propose a 6D object pose estimation method that can be trained with pure RGB images without any auxiliary information. We first obtain a rough pose initialization from networks trained on synthetic images rendered from the target's 3D mesh. Then, we introduce a refinement strategy leveraging the geometry constraint in synthetic-to-real image pairs from multiple different views. We formulate this geometry constraint as pixel-level flow consistency between the training images with dynamically generated pseudo labels. We evaluate our method on three challenging datasets and demonstrate that it outperforms state-of-the-art self-supervised methods significantly, with neither 2D annotations nor additional depth images.

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