English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recently, the community has made tremendous progress in developing effectivemethods for point cloud video understanding that learn from massive amounts oflabeled data. However, annotating point cloud videos is usually notoriouslyexpensive. Moreover, training via one or only a few traditional tasks (e.g.,classification) may be insufficient to learn subtle details of thespatio-temporal structure existing in point cloud videos. In this paper, wepropose a Masked Spatio-Temporal Structure Prediction (MaST-Pre) method tocapture the structure of point cloud videos without human annotations. MaST-Preis based on spatio-temporal point-tube masking and consists of twoself-supervised learning tasks. First, by reconstructing masked point tubes,our method is able to capture the appearance information of point cloud videos.Second, to learn motion, we propose a temporal cardinality differenceprediction task that estimates the change in the number of points within apoint tube. In this way, MaST-Pre is forced to model the spatial and temporalstructure in point cloud videos. Extensive experiments on MSRAction-3D,NTU-RGBD, NvGesture, and SHREC'17 demonstrate the effectiveness of the proposedmethod.

Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos