Occlusion-Robust Object Pose Estimation with Holistic Representation
- URL: http://arxiv.org/abs/2110.11636v1
- Date: Fri, 22 Oct 2021 08:00:26 GMT
- Title: Occlusion-Robust Object Pose Estimation with Holistic Representation
- Authors: Bo Chen, Tat-Jun Chin, Marius Klimavicius
- Abstract summary: State-of-the-art (SOTA) object pose estimators take a two-stage approach.
We develop a novel occlude-and-blackout batch augmentation technique.
We also develop a multi-precision supervision architecture to encourage holistic pose representation learning.
- Score: 42.27081423489484
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Practical object pose estimation demands robustness against occlusions to the
target object. State-of-the-art (SOTA) object pose estimators take a two-stage
approach, where the first stage predicts 2D landmarks using a deep network and
the second stage solves for 6DOF pose from 2D-3D correspondences. Albeit widely
adopted, such two-stage approaches could suffer from novel occlusions when
generalising and weak landmark coherence due to disrupted features. To address
these issues, we develop a novel occlude-and-blackout batch augmentation
technique to learn occlusion-robust deep features, and a multi-precision
supervision architecture to encourage holistic pose representation learning for
accurate and coherent landmark predictions. We perform careful ablation tests
to verify the impact of our innovations and compare our method to SOTA pose
estimators. Without the need of any post-processing or refinement, our method
exhibits superior performance on the LINEMOD dataset. On the YCB-Video dataset
our method outperforms all non-refinement methods in terms of the ADD(-S)
metric. We also demonstrate the high data-efficiency of our method. Our code is
available at http://github.com/BoChenYS/ROPE
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