  Existing learning-based point feature descriptors are usually task-agnostic,
which pursue describing the individual 3D point clouds as accurate as possible.
However, the matching task aims at describing the corresponding points
consistently across different 3D point clouds. Therefore these too accurate
features may play a counterproductive role due to the inconsistent point
feature representations of correspondences caused by the unpredictable noise,
partiality, deformation, \etc, in the local geometry. In this paper, we propose
to learn a robust task-specific feature descriptor to consistently describe the
correct point correspondence under interference. Born with an Encoder and a
Dynamic Fusion module, our method EDFNet develops from two aspects. First, we
augment the matchability of correspondences by utilizing their repetitive local
structure. To this end, a special encoder is designed to exploit two input
point clouds jointly for each point descriptor. It not only captures the local
geometry of each point in the current point cloud by convolution, but also
exploits the repetitive structure from paired point cloud by Transformer.
Second, we propose a dynamical fusion module to jointly use different scale
features. There is an inevitable struggle between robustness and
discriminativeness of the single scale feature. Specifically, the small scale
feature is robust since little interference exists in this small receptive
field. But it is not sufficiently discriminative as there are many repetitive
local structures within a point cloud. Thus the resultant descriptors will lead
to many incorrect matches. In contrast, the large scale feature is more
discriminative by integrating more neighborhood information. ...
