Is there any sample code for camera position optimization + mesh prediction via silhouette rendering

[rezhv]

❓ camera position optimization + mesh prediction via silhouette rendering

[albertotono]

Dear @rezhv , Do these tutorials work for you? https://github.com/facebookresearch/pytorch3d/tree/master/docs/tutorials

Specifically https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

[rezhv]

Dear @rezhv , Do these tutorials work for you? https://github.com/facebookresearch/pytorch3d/tree/master/docs/tutorials

Specifically https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

Thanks for your quick reply @albertotono What I am looking for is something that incorporates camera optimization and predicting meshes using silhouette at the same time. I want a model that I can train with only pictures and no 3d object to use as ground truth. I have the following model which trains with only one camera view

class Model(nn.Module): def init(self, meshes, renderer, image_ref): super().init()

    self.meshes = meshes

    self.device = meshes.device

    self.renderer = renderer

    # Get the silhouette of the reference RGB image by finding all the non zero values. 
    # image_ref = torch.from_numpy((image_ref1.astype(np.float32)))

    self.register_buffer('image_ref', image_ref)
    verts_shape = meshes.verts_packed().shape
    self.deform_verts = nn.Parameter(torch.full(verts_shape, 0.0, device=device, requires_grad=True))
    # Create an optimizable parameter for the x, y, z position of the camera. 
    self.register_buffer('camera_position', torch.from_numpy(np.array([3.0,  6.9, +2.5], dtype=np.float32)).to(meshes.device))
    # self.camera_position = nn.Parameter(
    #     torch.from_numpy(np.array([3.0,  6.9, +2.5], dtype=np.float32)).to(meshes.device))

def forward(self):

    loss = {k: torch.tensor(0.0, device=device) for k in losses}
    update_mesh_shape_prior_losses(self.meshes, loss)

    # Render the image using the updated camera position. Based on the new position of the 
    # camer we calculate the rotation and translation matrices

    R = look_at_rotation(self.camera_position[None, :], device=self.device)  # (1, 3, 3)
    T = -torch.bmm(R.transpose(1, 2), self.camera_position[None, :, None])[:, :, 0]   # (1, 3)

    self.meshes = src_mesh.offset_verts(self.deform_verts)

    for j in np.random.permutation(num_views).tolist():
      image = self.renderer(meshes_world=self.meshes.clone(), R=R, T=T)
      predicted_silhouette = image[..., 3]
      # print(predicted_silhouette.detach().cpu().numpy().shape)
      # print(self.image_ref.detach().cpu().numpy().shape)
      loss_silhouette = ((predicted_silhouette.squeeze() - self.image_ref.squeeze()) ** 2).mean()
      loss["silhouette"] += loss_silhouette / num_views

    # Weighted sum of the losses
    sum_loss = torch.tensor(0.0, device=device)
    for k, l in loss.items():
        sum_loss += l * losses[k]["weight"]
        losses[k]["values"].append(l)

    # Print the losses
    # loop.set_description("total_loss = %.6f" % sum_loss)

    # Calculate the silhouette loss

    # loss = torch.sum((image[..., 3] - self.image_ref) ** 2)/30000
    # loss = sum_loss + loss
    return sum_loss, image`

However, the edge loss does not seem to go down.

[rezhv]

[gkioxari]

First, @albertotono thank you for helping out and pointing to the tutorials! This is fantastic!!

@rezhv It's unclear to me whether you are bringing up a bug or a research question. Note that the issues of this repo should be focused on bugs or new feature requests. Of course, discussing solving research problems is important and maybe someone in here can help you with your optimization. However, the PyTorch3D does not have the capacity to consult for users' projects. We hope you find a solution to your problem and good luck with your project!

[xhuan8]

@rezhv did you solved this issue, I follow the same procedures but the silhouette loss goes up