Trans6D: Transformer-Based 6D Object Pose Estimation and Refinement

Zhang，Zhongqun1; Chen，Wei2; Zheng，Linfang1,3; Leonardis，Aleš1; Chang，Hyung Jin1

10.1007/978-3-031-25085-9_7

2023

0302-9743

1611-3349

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

13808 LNCS

112-128

Estimating 6D object pose from a monocular RGB image remains challenging due to factors such as texture-less and occlusion. Although convolution neural network (CNN)-based methods have made remarkable progress, they are not efficient in capturing global dependencies and often suffer from information loss due to downsampling operations. To extract robust feature representation, we propose a Transformer-based 6D object pose estimation approach (Trans6D). Specifically, we first build two transformer-based strong baselines and compare their performance: pure Transformers following the ViT (Trans6D-pure) and hybrid Transformers integrating CNNs with Transformers (Trans6D-hybrid). Furthermore, two novel modules have been proposed to make the Trans6D-pure more accurate and robust: (i) a patch-aware feature fusion module. It decreases the number of tokens without information loss via shifted windows, cross-attention, and token pooling operations, which is used to predict dense 2D-3D correspondence maps; (ii) a pure Transformer-based pose refinement module (Trans6D+) which refines the estimated poses iteratively. Extensive experiments show that the proposed approach achieves state-of-the-art performances on two datasets.

6D object pose estimation Transformer

Others

English

2-s2.0-85151354860

Scopus

1.University of Birmingham,Birmingham,United Kingdom
2.Defense Innovation Institute,Boston,China
3.SUSTech,Shenzhen,China

Zhang，Zhongqun,Chen，Wei,Zheng，Linfang,et al. Trans6D: Transformer-Based 6D Object Pose Estimation and Refinement[C],2023:112-128.