Shi, H., Liu, F., Wu, Z., Xu, Y., & Lin, G. (2025). Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Progressive 4D Grouping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(5), 3487–3499. https://doi.org/10.1109/tpami.2025.3532284
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L7b0021