In order to reduce the cost of manual operation, this paper proposed a fully convolutional neural network model for 6DoF pose and size estimation of targets with small data sets for 6DoF pose and size estimation that required collecting and labeling a large amount of data to train neural networks. Firstly, it combined the attention mechanism with the feature pyramid to extract the region of interest through the region suggestion network, and the region was input into the parallel fusion full convolution network to obtain the mask map. Secondly, it enriched the feature information after each convolution by adding jump connections, which were fused and classified to obtain the predicted normalization coordinate space map. Finally, it obtained the 6DoF pose and size of the target by 3D point cloud registration between the obtained mask image and the normalization coordinate space image. Experiments show that compared with PVN3D method, this method improves the accuracy by about 2.6% and GPVPose method by about 1% in small data sets.

[2022-09-14] Accepted Paper /doc/2022.06.0319/v1

[2023-03-05] Printed Article /doc/2022.06.0319/v2