I've got some problem understanding the code in transform_estimation.py

[fffire-oss]

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def est_quad_linear_robust(pts0, pts1, weight=None): pts0_curr = pts0 trans = torch.eye(4)

par = 1.0 if weight is None: weight = torch.ones(pts0.size()[0], 1)

for i in range(20): if i > 0 and i % 5 == 0: par /= 2.0

A, b = build_linear_system(pts0_curr, pts1, weight)
x = solve_linear_system(A, b)



trans_curr = get_trans(x)
pts0_curr = update_pcd(pts0_curr, trans_curr)
weight = compute_weights(pts0_curr, pts1, par)
trans = trans_curr.mm(trans)

return trans

As shown above, I couldn't get the point why we can attain the transformation matrix by iteration. The code of solve_linear system seems to be complicated. Can someone help me solve my confusion?

def build_linear_system(pts0, pts1, weight): npts0 = pts0.shape[0] A0 = torch.zeros((npts0, 6)) A1 = torch.zeros((npts0, 6)) A2 = torch.zeros((npts0, 6)) A0[:, 1] = pts0[:, 2] A0[:, 2] = -pts0[:, 1] A0[:, 3] = 1 A1[:, 0] = -pts0[:, 2] A1[:, 2] = pts0[:, 0] A1[:, 4] = 1 A2[:, 0] = pts0[:, 1] A2[:, 1] = -pts0[:, 0] A2[:, 5] = 1 ww1 = weight.repeat(3, 6) ww2 = weight.repeat(3, 1) A = ww1 * torch.cat((A0, A1, A2), 0) b = ww2 * torch.cat( (pts1[:, 0] - pts0[:, 0], pts1[:, 1] - pts0[:, 1], pts1[:, 2] - pts0[:, 2]), 0, ).unsqueeze(1) return A, b

def solve_linear_system(A, b): temp = torch.inverse(A.t().mm(A)) return temp.mm(A.t()).mm(b)