Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose
Estimation
- URL: http://arxiv.org/abs/2107.12549v1
- Date: Tue, 27 Jul 2021 01:55:30 GMT
- Title: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose
Estimation
- Authors: Yilin Wen, Xiangyu Li, Hao Pan, Lei Yang, Zheng Wang, Taku Komura,
Wenping Wang
- Abstract summary: We present a novel approach for scalable 6D pose estimation, by self-supervised learning on synthetic data of multiple objects using a single autoencoder.
We test our method on two multi-object benchmarks with real data, T-LESS and NOCS REAL275, and show it outperforms existing RGB-based methods in terms of pose estimation accuracy and generalization.
- Score: 44.8872454995923
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 6D pose estimation of rigid objects from a single RGB image has seen
tremendous improvements recently by using deep learning to combat complex
real-world variations, but a majority of methods build models on the per-object
level, failing to scale to multiple objects simultaneously. In this paper, we
present a novel approach for scalable 6D pose estimation, by self-supervised
learning on synthetic data of multiple objects using a single autoencoder. To
handle multiple objects and generalize to unseen objects, we disentangle the
latent object shape and pose representations, so that the latent shape space
models shape similarities, and the latent pose code is used for rotation
retrieval by comparison with canonical rotations. To encourage shape space
construction, we apply contrastive metric learning and enable the processing of
unseen objects by referring to similar training objects. The different
symmetries across objects induce inconsistent latent pose spaces, which we
capture with a conditioned block producing shape-dependent pose codebooks by
re-entangling shape and pose representations. We test our method on two
multi-object benchmarks with real data, T-LESS and NOCS REAL275, and show it
outperforms existing RGB-based methods in terms of pose estimation accuracy and
generalization.
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