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Ongoing project with University of Toronto. Final improved version will be published in a new repository

List of upgrades compared to KPConv-Pytorch

  • New pytorch implementation of grid subsampling on GPU tensors using torch.unique function.
  • New pytorch implementation of cropped radius neighbor on GPU tensors using Keops.
  • New network blocks, simplified and more standalone
  • Separate block for KPConv, KPDef and KPInv blocks
  • More standalone blocks for easir plug and play in other networks and tasks
  • Configuration as an easyDict and save as json for readability
  • Option to choose from two input pipelines:
    1. Simpler input pipeline that does not preload neighborhoods.
      • With it, a network that computes conv neighbors on the fly on GPU.
      • Faster for inference on streamed data (like segmenting lidar frames in real time).
      • Easier to use as plug and play in other types of networks that do not compute neighbors during data preparation.
      • Smaller use of CPU.
    2. Old pipeline precomputing neighbors in parallel on GPU.
      • The neighbors are computed in advance by the dataloader on CPU.
      • Much faster training or test when data is already on disk and input preparation can be parallelized.
      • Possible heavy CPU usage.
  • New simplified and faster calibration for batch limit and neighbor limits.
  • Additional options like feature grouping.
  • New definition of architecture layers and blocks.
  • Still the option

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