This is a framework for running common deep learning models for point cloud analysis tasks against classic benchmark. It heavily relies on pytorch geometric and hydra core.

Documentation | Pytorch Geometric | Facebook Hydra

The framework allows lean and yet complex model to be built with minimum effort and great reproducibility.

COMPACT API

# PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (https://arxiv.org/abs/1706.02413)
# Credit Charles R. Qi: https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_part_seg_msg_one_hot.py

pointnet2_onehot:
      type: pointnet2_dense
      conv_type: "DENSE"
      use_category: True
      down_conv:
          module_name: PointNetMSGDown
          npoint: [1024, 256, 64, 16]
          radii: [[0.05, 0.1], [0.1, 0.2], [0.2, 0.4], [0.4, 0.8]]
          nsamples: [[16, 32], [16, 32], [16, 32], [16, 32]]
          down_conv_nn:
              [
                  [[FEAT, 16, 16, 32], [FEAT, 32, 32, 64]],
                  [[32 + 64, 64, 64, 128], [32 + 64, 64, 96, 128]],
                  [[128 + 128, 128, 196, 256], [128 + 128, 128, 196, 256]],
                  [[256 + 256, 256, 256, 512], [256 + 256, 256, 384, 512]],
              ]
      up_conv:
          module_name: DenseFPModule
          up_conv_nn:
              [
                  [512 + 512 + 256 + 256, 512, 512],
                  [512 + 128 + 128, 512, 512],
                  [512 + 64 + 32, 256, 256],
                  [256 + FEAT, 128, 128],
              ]
          skip: True
      mlp_cls:
          nn: [128, 128]
          dropout: 0.5

Getting started

Requirements:

• CUDA > 10
• Python 3 + headers (python-dev)
• Poetry (Optional but highly recommended)

Setup repo

Clone the repo to your local machine then run the following command from the root of the repo

poetry install

This will install all required dependencies in a new virtual environment.

Activate it

poetry shell

You can check that the install has been successful by running

python -m unittest

Train pointnet++ on part segmentation task for dataset shapenet

poetry run python train.py experiment.model_name=pointnet2_charlesssg wandb.log=False experiment.data=shapenet

And you should see something like that

Benchmark

S3DIS

Model Name	Size	Speed Train / Test	Cross Entropy	OAcc	mIou	mAcc
pointnet2_original	3,026,829	04:29 / 01:07	0.0512	85.26	45.58	73.11

Shapenet part segmentation

The data reported below correspond to the part segmentation problem for Shapenet for all categories. We report against mean instance IoU and mean class IoU (average of the mean instance IoU per class)

Model Name	Use Normals	Size	Speed Train / Test	Cross Entropy	OAcc	mIou
pointnet2_charlesmsg	Yes	1,733,946	15:07 / 01:20	0.089	82.1	85.1

Contributing

Contributions are welcome! The only asks are that you stick to the styling and that you add tests as you add more features! For styling you can use pre-commit hooks to help you:

pre-commit install

A sequence of checks will be run for you and you may have to add the fixed files again to the stahed files.

Contributers

• Thomas Chaton
• Nicolas Chaulet
• Tristan Heywood