mlsanityData to 14 Oct 2026

Deep Convolution for Irregularly Sampled Temporal Point Clouds

arXiv 2105.00137 ↗First posted 1 May 2021cs.LGcited by 1

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Abstract (arXiv)

We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time. Such processes occur in sensor networks, citizen science, multi-robot systems, and many others. We propose a new deep model that is able to directly learn and predict over this irregularly sampled data, without voxelization, by leveraging a recent convolutional architecture for static point clouds. The model also easily incorporates the notion of multiple entities in the process. In particular, the model can flexibly answer prediction queries about arbitrary space-time points for different entities regardless of the distribution of the training or test-time data. We present experiments on real-world weather station data and battles between large armies in StarCraft II. The results demonstrate the model's flexibility in answering a variety of query types and demonstrate improved performance and efficiency compared to state-of-the-art baselines.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 25 references that are in the map. First posted on arXiv 1 May 2021.

Builds on

22 of 25 references.

References · 22

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  5. 2017Deep Setscited by 1,421
  6. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
  7. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  8. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  9. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  10. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 509
  11. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 310
  12. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 274
10 more
  1. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  2. 2020Transformer Hawkes Processcited by 235
  3. 2017Submanifold Sparse Convolutional Networkscited by 223
  4. 2018SPLATNet: Sparse Lattice Networks for Point Cloud Processingcited by 158
  5. 2019Set Functions for Time Seriescited by 138
  6. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 122
  7. 2019Interpolation-Prediction Networks for Irregularly Sampled Time Seriescited by 89
  8. 2018Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Cloudscited by 62
  9. 2018Learning Temporal Point Processes via Reinforcement Learningcited by 40
  10. 2019Self-Attentive Hawkes Processescited by 23

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Citing papers per year: 2022: 1.

1 paper.

  1. 2022Unsupervised Hebbian Learning on Point Sets in StarCraft IIcited by 3