Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
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
Cite
@article{arxiv.2203.17234,
title = {Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions},
author = {Van Nguyen Nguyen and Yinlin Hu and Yang Xiao and Mathieu Salzmann and Vincent Lepetit},
journal= {arXiv preprint arXiv:2203.17234},
year = {2022}
}
Comments
CVPR 2022