ambitious-octopus / pointconv-tensorflow2

PointConv modules implemented as tensorflow 2 keras layers.

A fork of PointConv for tensorflow 2.8 and CUDA 11.2

What are the differences from dgriffiths3 repo?

1. This implementation works with tensorlfow 2.8 and cuda 11.2 (Nvidia 3080 GPUs only support CUDA >= 11.2).
2. I have also added a simpler import method: from pointconv import PointConvSA.

Setup

1. Clone this repository
2. cd into pointconv-tensorflow2
3. Compile ops by running ./tf_ops/compile_ops.sh PYTHON_INTERPRETER_PATH (ensure the CUDA_ROOT points correctly to cuda 11.2)
4. Install pointconv package with python -m pip install -e .

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This repository containts implementations of the PointConv (Wu et al, 2019) feature encoder and feature decoder layers as tf.keras.layers classes. This allows for PointConv layers to be used as part of the standard tf.keras api. The repository does not aim to be an exact implementation of the original repostiroy, rather a useful tool for building custom models or simple backend encoders for unordered point sets. For more details regarding the technical details check out the original paper and github page. The implementation also matches the style of the PointNet++ keras layers.

Usage

The layers follow the standard tf.keras.layers api. To import in your own project, copy the pointconv and tf_ops folders and set a relative path to find the layers. Here is an example of how a simple PointConv SetAbstraction model can be built using tf.keras.Model().

from tensorflow import keras
from pointconv import PointConvSA

class MyModel(keras.Model):

  def __init__(self, batch_size):
    super(MyModel, self).__init__()

        self.layer1 = PointConvSA(npoint=512, radius=0.1, sigma=0.1, K=32, mlp=[64, 64, 128], bn=True)
        self.layer2 = PointConvSA(npoint=128, radius=0.2, sigma=0.2, K=32, mlp=[128, 128, 256], bn=True)
        self.layer2 = PointConvSA(npoint=1, radius=0.8, sigma=0.4, K=32, mlp=[256, 512, 1024], group_all=True bn=True)

        # To make a classifier, just add some fully-connected layers

        self.fn1 = keras.layers.Dense(512)
        self.fn2 = keras.layers.Dense(256)
        self.fn3 = keras.layers.Dense(n_classes, tf.nn.softmax)
    
  def call(input):

    xyz, points = self.layer1(input, None, training=training)
    xyz, points = self.layer2(xyz, points, training=training)
    xyz, points = self.layer3(xyz, points, training=training)

    net = tf.reshape(points, (self.batch_size, -1))

    net = self.dense1(net)
    net = self.dense2(net)
    pred = self.dense3(net)

    return pred

Examples

Refer to dgriffiths3 repository for use cases.

Note

If you use these layers in your project remember to cite the original authors:

@article{wu2018pointconv,
  title={PointConv: Deep Convolutional Networks on 3D Point Clouds},
  author={Wu, Wenxuan and Qi, Zhongang and Fuxin, Li},
  journal={arXiv preprint arXiv:1811.07246},
  year={2018}
}