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.

SSFlowNet 是一种半监督的场景流估计方法，通过混合有标签和无标签的数据，优化标签成本和模型训练精度之间的平衡，主要通过创新地使用伪标签降低对大量标记数据的依赖，并强调点云的几何结构以及引入空间记忆特征来学习顺序时间帧上点之间的几何关系，从而动态构建相关性矩阵以评估每个个体点的场景流依赖性，并通过在 SSFlowNet 中集成流一致性模块，提高了流的一致性估计能力。实证结果表明，SSFlowNet 在伪标签生成方面优于现有方法，适应不同的数据量，并且在场景流估计领域取得了显著的进展。