  In many vision and robotics applications, it is common that the captured
objects are represented by very few points. Most of the existing completion
methods are designed for partial point clouds with many points, and they
perform poorly or even fail completely in the case of few points. However, due
to the lack of detail information, completing objects from few points faces a
huge challenge. Inspired by the successful applications of GAN and Transformers
in the image-based vision task, we introduce GAN and Transformer techniques to
address the above problem. Firstly, the end-to-end encoder-decoder network with
Transformers and the Wasserstein GAN with Transformer are pre-trained, and then
the overall network is fine-tuned. Experimental results on the ShapeNet dataset
show that our method can not only improve the completion performance for many
input points, but also keep stable for few input points. Our source code is
available at https://github.com/WxfQjh/Stability-point-recovery.git.
