Method forward

preprocessing/preprocessing_utils.py:272–296

(self, points)

        self.register_parameter('translation', translation)

    def forward(self, points):
        device = points.device
        with torch.no_grad():
            camera_mat = torch.zeros([self.batch_size, 2, 2],
                                     dtype=self.dtype, device=points.device)
            camera_mat[:, 0, 0] = self.focal_length_x
            camera_mat[:, 1, 1] = self.focal_length_y

        camera_transform = transform_mat(self.rotation,
                                         self.translation.unsqueeze(dim=-1))

        homog_coord = torch.ones(list(points.shape)[:-1] + [1],
                                 dtype=points.dtype,
                                 device=device)
        # Convert the points to homogeneous coordinates
        points_h = torch.cat([points, homog_coord], dim=-1)

        projected_points = torch.einsum('bki,bji->bjk',
                                        [camera_transform, points_h])

        img_points = torch.div(projected_points[:, :, :2],
                               projected_points[:, :, 2].unsqueeze(dim=-1))
        img_points = torch.einsum('bki,bji->bjk', [camera_mat, img_points]) \
            + self.center.unsqueeze(dim=1)
        return img_points
    
class Renderer():