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Showing content from https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.PPFConv.html below:

torch_geometric.nn.conv.PPFConv — pytorch_geometric documentation

class PPFConv(local_nn: Optional[Callable] = None, global_nn: Optional[Callable] = None, add_self_loops: bool = True, **kwargs)[source]

Bases: MessagePassing

The PPFNet operator from the “PPFNet: Global Context Aware Local Features for Robust 3D Point Matching” paper.

\[\mathbf{x}^{\prime}_i = \gamma_{\mathbf{\Theta}} \left( \max_{j \in \mathcal{N}(i) \cup \{ i \}} h_{\mathbf{\Theta}} ( \mathbf{x}_j, \| \mathbf{d_{j,i}} \|, \angle(\mathbf{n}_i, \mathbf{d_{j,i}}), \angle(\mathbf{n}_j, \mathbf{d_{j,i}}), \angle(\mathbf{n}_i, \mathbf{n}_j) \right)\]

where \(\gamma_{\mathbf{\Theta}}\) and \(h_{\mathbf{\Theta}}\) denote neural networks, .i.e. MLPs, which takes in node features and torch_geometric.transforms.PointPairFeatures.

Parameters:

• local_nn (torch.nn.Module, optional) – A neural network \(h_{\mathbf{\Theta}}\) that maps node features x and relative spatial coordinates pos_j - pos_i of shape [-1, in_channels + num_dimensions] to shape [-1, out_channels], e.g., defined by torch.nn.Sequential. (default: None)

• global_nn (torch.nn.Module, optional) – A neural network \(\gamma_{\mathbf{\Theta}}\) that maps aggregated node features of shape [-1, out_channels] to shape [-1, final_out_channels], e.g., defined by torch.nn.Sequential. (default: None)

• add_self_loops (bool, optional) – If set to False, will not add self-loops to the input graph. (default: True)

• **kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.

Shapes:

• input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, positions \((|\mathcal{V}|, 3)\) or \(((|\mathcal{V_s}|, 3), (|\mathcal{V_t}|, 3))\) if bipartite, point normals \((|\mathcal{V}, 3)\) or \(((|\mathcal{V_s}|, 3), (|\mathcal{V_t}|, 3))\) if bipartite, edge indices \((2, |\mathcal{E}|)\)

• output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V}_t|, F_{out})\) if bipartite

forward(x: Union[Tensor, None, Tuple[Optional[Tensor], Optional[Tensor]]], pos: Union[Tensor, Tuple[Tensor, Tensor]], normal: Union[Tensor, Tuple[Tensor, Tensor]], edge_index: Union[Tensor, SparseTensor]) → Tensor[source]

Runs the forward pass of the module.

Return type:

Tensor

reset_parameters()[source]

Resets all learnable parameters of the module.

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