CorrelationFlow: A Training-Free Geometric Approach for LiDAR Scene Flow Estimation
LiDAR scene flow is the task of estimating point-wise motion from sequential point clouds. It is essential for autonomous driving, as it provides critical information about moving objects and their trajectories. In recent years, unsupervised methods have gained significant traction by eliminating the need for expensive point-wise annotations. However, despite incremental performance improvements, these methods uniformly rely on a single paradigm: training feed-forward networks through optimizing a loss function based on the Chamfer distance, a framework that inherently limits their progression. This paper presents CorrelationFlow, a new paradigm that reformulates LiDAR scene flow as a cor...
| Authors | Minh-Quan Dao, Yancong Lin, Julie Stephany Berrio Perez et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 July 2026 |
| Last updated at source | 14 October 2026 |
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