Introduction

We shortly introduce the fundamental concepts of TorchSparse through self-contained examples.

Sparse Tensor

Sparse tensor (SparseTensor) is the main data structure for point cloud, which has two data fields:

• Coordinates (coords): a 2D integer tensor with a shape of N x 4, where the first three dimensions correspond to quantized x, y, z coordinates, and the last dimension denotes the batch index.

• Features (feats): a 2D tensor with a shape of N x C, where C is the number of feature channels.

Most existing datasets provide raw point cloud data with float coordinates. We can use sparse_quantize (provided in torchsparse.utils.quantize) to voxelize x, y, z coordinates and remove duplicates:

coords -= np.min(coords, axis=0, keepdims=True)
coords, indices = sparse_quantize(coords, voxel_size, return_index=True)
coords = torch.tensor(coords, dtype=torch.int)
feats = torch.tensor(feats[indices], dtype=torch.float)
tensor = SparseTensor(coords=coords, feats=feats)

We can then use sparse_collate_fn (provided in torchsparse.utils.collate) to assemble a batch of SparseTensor’s (and add the batch dimension to coords). Please refer to this example for more details.

Sparse Neural Network

The neural network interface in TorchSparse is very similar to PyTorch:

from torch import nn
from torchsparse import nn as spnn

model = nn.Sequential(
    spnn.Conv3d(in_channels, out_channels, kernel_size),
    spnn.BatchNorm(out_channels),
    spnn.ReLU(True),
)