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In the domain of supervised scene flow estimation, the process of manuallabeling is both time-intensive and financially demanding. This paperintroduces SSFlowNet, a semi-supervised approach for scene flow estimation,that utilizes a blend of labeled and unlabeled data, optimizing the balancebetween the cost of labeling and the precision of model training. SSFlowNetstands out through its innovative use of pseudo-labels, mainly reducing thedependency on extensively labeled datasets while maintaining high modelaccuracy. The core of our model is its emphasis on the intricate geometricstructures of point clouds, both locally and globally, coupled with a novelspatial memory feature. This feature is adept at learning the geometricrelationships between points over sequential time frames. By identifyingsimilarities between labeled and unlabeled points, SSFlowNet dynamicallyconstructs a correlation matrix to evaluate scene flow dependencies atindividual point level. Furthermore, the integration of a flow consistencymodule within SSFlowNet enhances its capability to consistently estimate flow,an essential aspect for analyzing dynamic scenes. Empirical results demonstratethat SSFlowNet surpasses existing methods in pseudo-label generation and showsadaptability across varying data volumes. Moreover, our semi-supervisedtraining technique yields promising outcomes even with different smaller ratiolabeled data, marking a substantial advancement in the field of scene flowestimation.

SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label