  Occlusions hinder point cloud frame alignment in LiDAR data, a challenge
inadequately addressed by scene flow models tested mainly on occlusion-free
datasets. Attempts to integrate occlusion handling within networks often suffer
accuracy issues due to two main limitations: a) the inadequate use of occlusion
information, often merging it with flow estimation without an effective
integration strategy, and b) reliance on distance-weighted upsampling that
falls short in correcting occlusion-related errors. To address these
challenges, we introduce the Correlation Matrix Upsampling Flownet
(CMU-Flownet), incorporating an occlusion estimation module within its cost
volume layer, alongside an Occlusion-aware Cost Volume (OCV) mechanism.
Specifically, we propose an enhanced upsampling approach that expands the
sensory field of the sampling process which integrates a Correlation Matrix
designed to evaluate point-level similarity. Meanwhile, our model robustly
integrates occlusion data within the context of scene flow, deploying this
information strategically during the refinement phase of the flow estimation.
The efficacy of this approach is demonstrated through subsequent experimental
validation. Empirical assessments reveal that CMU-Flownet establishes
state-of-the-art performance within the realms of occluded Flyingthings3D and
KITTY datasets, surpassing previous methodologies across a majority of
evaluated metrics.
