aidsorb.models

This module provides deep learning architectures for point cloud processing.

Note

Currently, only PointNet is implemented, a lightweight version of the original architecture where the TNet’s for input and feature transforms have been removed.

Warning

It is recommended to use batched inputs in all cases. For example, even if a single pcd of shape (3+C, N) is to be processed with PointNet, reshape it to (1, 3+C, N). One way to you can do it is the following: pcd = pcd.unsqueeze(0).

Todo

Add more architectures for point cloud processing.

References

[PointNet]

R. Q. Charles, H. Su, M. Kaichun and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 77-85, doi: 10.1109/CVPR.2017.16.

class aidsorb.models.PointNet(head, local_feats=False, n_global_feats=1024)

Bases: Module

Vanilla version from the [PointNet] paper where TNet’s have been removed.

PointNet takes as input a point cloud and produces one or more outputs. The type of the task is determined by head.

Currently implemented heads include:

1. PointNetClsHead: classification and regression

2. PointNetSegHead: segmentation

The input must be batched, i.e. have shape of (B, C, N) where B is the batch size, C is the number of input channels and N is the number of points in each point cloud.

Tip

You can define a custom_head head as a torch.nn.Module and pass it to head.

If local_features=False, the input to custom_head must have the same shape as in PointNetClsHead.forward(). Otherwise, the input to custom_head must have the same shape as in PointNetSegHead.forward().

Parameters:

• head (torch.nn.Module)

• local_feats (bool, default=False)

• n_global_feats (int, default=1024)

See also

PointNetBackbone

For a description of local_feats and n_global_feats.

Examples

>>> from aidsorb.modules import PointNetClsHead, PointNetSegHead
>>> cls_head = PointNetClsHead(n_outputs=2)
>>> seg_head = PointNetSegHead(n_outputs=10)
>>> x = torch.randn(32, 4, 300)

>>> cls_net = PointNet(head=cls_head, n_global_feats=256)
>>> cls_net(x).shape
torch.Size([32, 2])
>>> cls_net.backbone(x)[1].shape  # Critical indices.
torch.Size([32, 256])

>>> seg_net = PointNet(head=seg_head, n_global_feats=512, local_feats=True)
>>> seg_net(x).shape
torch.Size([32, 300, 10])
>>> seg_net.backbone(x)[1].shape  # Critical indices.
torch.Size([32, 512])

forward(x)

Run the forward pass.

Parameters:

x (tensor of shape (B, C, N))

Returns:

out – The output of head.

Return type:

tensor