OSOP: A Multi-Stage One Shot Object Pose Estimation Framework

• URL: http://arxiv.org/abs/2203.15533v2
• Date: Wed, 30 Mar 2022 07:31:14 GMT
• Title: OSOP: A Multi-Stage One Shot Object Pose Estimation Framework
• Authors: Ivan Shugurov, Fu Li, Benjamin Busam, Slobodan Ilic
• Abstract summary: We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects. At test time, it takes as input a target image and a textured 3D query model. We evaluate the method on LineMOD, Occlusion, Homebrewed, YCB-V and TLESS datasets.
• Score: 35.89334617258322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects. At test time, it takes as input a target image and a textured 3D query model. The core idea is to represent a 3D model with a number of 2D templates rendered from different viewpoints. This enables CNN-based direct dense feature extraction and matching. The object is first localized in 2D, then its approximate viewpoint is estimated, followed by dense 2D-3D correspondence prediction. The final pose is computed with PnP. We evaluate the method on LineMOD, Occlusion, Homebrewed, YCB-V and TLESS datasets and report very competitive performance in comparison to the state-of-the-art methods trained on synthetic data, even though our method is not trained on the object models used for testing.

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