  Low-overlap regions between paired point clouds make the captured features
very low-confidence, leading cutting edge models to point cloud registration
with poor quality. Beyond the traditional wisdom, we raise an intriguing
question: Is it possible to exploit an intermediate yet misaligned image
between two low-overlap point clouds to enhance the performance of cutting-edge
registration models? To answer it, we propose a misaligned image supported
registration network for low-overlap point cloud pairs, dubbed ImLoveNet.
ImLoveNet first learns triple deep features across different modalities and
then exports these features to a two-stage classifier, for progressively
obtaining the high-confidence overlap region between the two point clouds.
Therefore, soft correspondences are well established on the predicted overlap
region, resulting in accurate rigid transformations for registration. ImLoveNet
is simple to implement yet effective, since 1) the misaligned image provides
clearer overlap information for the two low-overlap point clouds to better
locate overlap parts; 2) it contains certain geometry knowledge to extract
better deep features; and 3) it does not require the extrinsic parameters of
the imaging device with respect to the reference frame of the 3D point cloud.
Extensive qualitative and quantitative evaluations on different kinds of
benchmarks demonstrate the effectiveness and superiority of our ImLoveNet over
state-of-the-art approaches.
