# point_cloud1 and point_cloud2 represent the same 3D structure
# even though they are represented differently in memory
point_cloud1 = [(x1, y1, z1), (x2, y2, z2),..., (xn, yn, zn)]
point_cloud2 = [(x2, y2, z2), (x1, y1, z1),..., (xn, yn, zn)]

#--------- Pytorch pseudo-code for Structure Generator ---------#
class Structure_Generator(nn.Module):
    # contains two module in sequence, an encoder and a decoder
    def __init__(self):
        self.encoder = Encoder()
        self.decoder = Decoder()    def forward(self, RGB_image):
        # Encoder takes in one RGB image and 
        # output an encoded deep shape-embedding
        shape_embedding = self.encoder(RGB_image)
        
        # Decoder takes the encoded values and output  
        # multiples 2D projection (XYZ + mask)
        XYZ, maskLogit = self.decoder(shape_embedding)
 
       return XYZ, maskLogit

# --------- Pytorch pseudo-code for training loop ----------## Create 2D Conv Structure generator
model = Structure_Generator()
# only need to learn the 2D structure optimizer
optimizer = optim.SGD(model.parameters())# 2D projections from predetermined viewpoints
XYZ, maskLogit = model(RGB_images)# fused point cloud
#fuseTrans is predetermined viewpoints info
XYZid, ML = fuse3D(XYZ, maskLogit, fuseTrans)# Render new depth images at novel viewpoints
# renderTrans is novel viewpoints info
newDepth, newMaskLogit, collision = render2D(XYZid, ML, renderTrans)# Compute loss between novel view and ground truth
loss_depth = L1Loss()(newDepth, GTDepth)
loss_mask = BCEWithLogitLoss()(newMaskLogit, GTMask)
loss_total = loss_depth + loss_mask# Back-propagation to update Structure Generator
loss_total.backward()
optimizer.step()