Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints
  Voting for Robust 6D Object Pose Estimation
Jun Zhou Kai Chen Linlin Xu Qi Dou Jing Qin
One critical challenge in 6D object pose estimation from a single RGBD image
is efficient integration of two different modalities, i.e., color and depth. In
this work, we tackle this problem by a novel Deep Fusion Transformer~(DFTr)
block that can aggregate cross-modality features for improving pose estimation.
Unlike existing fusion methods, the proposed DFTr can better model
cross-modality semantic correlation by leveraging their semantic similarity,
such that globally enhanced features from different modalities can be better
integrated for improved information extraction. Moreover, to further improve
robustness and efficiency, we introduce a novel weighted vector-wise voting
algorithm that employs a non-iterative global optimization strategy for precise
3D keypoint localization while achieving near real-time inference. Extensive
experiments show the effectiveness and strong generalization capability of our
proposed 3D keypoint voting algorithm. Results on four widely used benchmarks
also demonstrate that our method outperforms the state-of-the-art methods by
large margins.