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