Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions

• URL: http://arxiv.org/abs/2203.17234v1
• Date: Thu, 31 Mar 2022 17:50:35 GMT
• Title: Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
• Authors: Van Nguyen Nguyen, Yinlin Hu, Yang Xiao, Mathieu Salzmann, Vincent Lepetit
• Abstract summary: We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. It relies on a small set of training objects to learn local object representations. We are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets.
• Score: 79.34847067293649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects. In contrast with the state-of-the-art methods, the new objects on which our method is applied can be very different from the training objects. As a result, we are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous template-based approaches further confirms the benefits of local features for template matching. We outperform the state-of-the-art template matching methods on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data are publicly available at https://github.com/nv-nguyen/template-pose

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