To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task.
The student network takes the incomplete one as input and restores the corresponding complete shape.
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface.
In this work, instead of using a global feature to recover the whole complete surface, we explore the functionality of multi-level features and aggregate different features to represent the known part and the missing part separately.
Point cloud based retrieval for place recognition is an emerging problem in vision field.