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.

在 LiDAR 数据中，遮挡对点云帧对齐造成了困扰，而现有的场景流模型主要在无遮挡的数据集上测试，未能充分解决这一挑战。为了应对这些问题，我们提出了一个新的方法 ——Correlation Matrix Upsampling Flownet (CMU-Flownet)，该方法在代价体积层中引入了一个遮挡估计模块，并在其中引入了一个遮挡感知的代价体积机制 (OCV)。通过实验证明，CMU-Flownet 在遮挡的 Flyingthings3D 和 KITTY 数据集上取得了最先进的性能，超过了以往方法在大多数评估指标上的表现。