  In the domain of supervised scene flow estimation, the process of manual
labeling is both time-intensive and financially demanding. This paper
introduces SSFlowNet, a semi-supervised approach for scene flow estimation,
that utilizes a blend of labeled and unlabeled data, optimizing the balance
between the cost of labeling and the precision of model training. SSFlowNet
stands out through its innovative use of pseudo-labels, mainly reducing the
dependency on extensively labeled datasets while maintaining high model
accuracy. The core of our model is its emphasis on the intricate geometric
structures of point clouds, both locally and globally, coupled with a novel
spatial memory feature. This feature is adept at learning the geometric
relationships between points over sequential time frames. By identifying
similarities between labeled and unlabeled points, SSFlowNet dynamically
constructs a correlation matrix to evaluate scene flow dependencies at
individual point level. Furthermore, the integration of a flow consistency
module within SSFlowNet enhances its capability to consistently estimate flow,
an essential aspect for analyzing dynamic scenes. Empirical results demonstrate
that SSFlowNet surpasses existing methods in pseudo-label generation and shows
adaptability across varying data volumes. Moreover, our semi-supervised
training technique yields promising outcomes even with different smaller ratio
labeled data, marking a substantial advancement in the field of scene flow
estimation.
