Spatial feature mapping for 6DoF object pose estimation

Highlights：

• We explore the possibility of solving the rotation ambiguous and occlusion problems in 6D pose estimation by using the spherical correlation and graph convolution. A robust 6D object pose estimation system is proposed.

• We propose to map the 2D convolutional feature to both a sphere and the 3D mesh representation. And the corresponding network components are integrated which forms the end-to-end deep neural network training and inference scheme. The proposed method digs more essential information from the data representation and processing. To our knowledge, this is new and meaningful for 6D object pose estimation.

• Target on the rotation ambiguous and occlusion challenges, we propose to solve from the parameter space and data structure. The state-of-the-art performance demonstrates the efficiency of the proposed system.