  In recent years, the LiDAR images, as a 2D compact representation of 3D LiDAR
point clouds, are widely applied in various tasks, e.g., 3D semantic
segmentation, LiDAR point cloud compression (PCC). Among these works, the
optical flow estimation for LiDAR image sequences has become a key issue,
especially for the motion estimation of the inter prediction in PCC. However,
the existing optical flow estimation models are likely to be unreliable for
LiDAR images. In this work, we first propose a light-weight flow estimation
model for LiDAR image sequences. The key novelty of our method lies in two
aspects. One is that for the different characteristics (with the
spatial-variation feature distribution) of the LiDAR images w.r.t. the normal
color images, we introduce the attention mechanism into our model to improve
the quality of the estimated flow. The other one is that to tackle the lack of
large-scale LiDAR-image annotations, we present an unsupervised method, which
directly minimizes the inconsistency between the reference image and the
reconstructed image based on the estimated optical flow. Extensive experimental
results have shown that our proposed model outperforms other mainstream models
on the KITTI dataset, with much fewer parameters.
