cgarciae/point-cloud-mnist

Point CLoud MNIST

A point cloud version of the original MNIST.

Getting Started

import matplotlib.pyplot as plt
import numpy as np

from datasets import load_dataset

# load dataset
dataset = load_dataset("cgarciae/point-cloud-mnist")
dataset.set_format("np")


# get numpy arrays
X_train = dataset["train"]["points"]
y_train = dataset["train"]["label"]
X_test = dataset["test"]["points"]
y_test = dataset["test"]["label"]


# plot some training samples
figure = plt.figure(figsize=(10, 10))
for i in range(3):
    for j in range(3):
        k = 3 * i + j
        plt.subplot(3, 3, k + 1)
        idx = np.random.randint(0, len(X_train))

        plt.title(f"{y_train[idx]}")
        plt.scatter(X_train[idx, :, 0], X_train[idx, :, 1])

plt.show()

Format

• points: (batch, point, 3) array of uint8.
• label: (batch, 1) array of uint8.

Where point is the number of points in the point cloud. Points have no order and were shuffled when creating the data. Each point has the structure [x, y, v] where:

• x: is the x coordinate of the point in the image.
• y: is the y coordinate of the point in the image.
• v: is the value of the pixel at the point in the image.

Samples are padded with 0s such that point = 351 since its the largest number of non-zero pixels per image in the original dataset. You can tell apart padding point because they are the only ones where v = 0.

Here is the distribution of non-zero pixels in the MNIST: