Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Progressive 4D Grouping.

Recently, some weakly supervised 3D point cloud segmentation methods have been proposed to develop effective models with minimum annotation efforts. Our previous work, W4DTS, proposes a challenging task that utilizes only 0.001% points in outdoor point cloud datasets to achieve an effective segmentation model. However, under an extremely limited annotation budget, the quality of pseudo labels generated by W4DTS is unsatisfactory, which limits the segmentation performance in such scenarios. To solve this issue, we propose a progressive 4D grouping approach to group the annotated and unannotated points across space and time, which can generate high-quality pseudo labels with very sparse annotated points. Moreover, to further improve our progressive 4D grouping approach, we design a cross-frame contrastive learning and a local consistency learning to improve the quality of our 4D grouping. Experimental results reveal that with only 0.001% annotations, our solution significantly outperforms the previous best approach on SemanticKITTI. We also evaluate our framework on the SemanticPOSS dataset and ScribbleKITTI dataset, and achieve performances close to our fully supervised backbone models.