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Summary: Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/553 Reviewed By: theschnitz Differential Revision: D26257591 Pulled By: gkioxari fbshipit-source-id: 899a3f733a77361e8572b0900a34b55764ff08f2
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@ -108,6 +108,7 @@ def mesh_laplacian_smoothing(meshes, method: str = "uniform"):
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idx = norm_w > 0
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norm_w[idx] = 1.0 / norm_w[idx]
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else:
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L_sum = torch.sparse.sum(L, dim=1).to_dense().view(-1, 1)
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norm_w = 0.25 * inv_areas
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else:
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raise ValueError("Method should be one of {uniform, cot, cotcurv}")
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@ -117,7 +118,7 @@ def mesh_laplacian_smoothing(meshes, method: str = "uniform"):
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elif method == "cot":
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loss = L.mm(verts_packed) * norm_w - verts_packed
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elif method == "cotcurv":
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loss = (L.mm(verts_packed) - verts_packed) * norm_w
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loss = (L.mm(verts_packed) - L_sum * verts_packed) * norm_w
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loss = loss.norm(dim=1)
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loss = loss * weights
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@ -89,11 +89,12 @@ class TestLaplacianSmoothing(unittest.TestCase):
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inv_areas[idx] = 1.0 / inv_areas[idx]
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norm_w = L.sum(dim=1, keepdims=True)
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L_sum = norm_w.clone()
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idx = norm_w > 0
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norm_w[idx] = 1.0 / norm_w[idx]
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if method == "cotcurv":
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loss = (L.mm(verts_packed) - verts_packed) * inv_areas * 0.25
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loss = (L.mm(verts_packed) - L_sum * verts_packed) * inv_areas * 0.25
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loss = loss.norm(dim=1)
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else:
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loss = L.mm(verts_packed) * norm_w - verts_packed
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@ -147,7 +148,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
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def test_laplacian_smoothing_cot(self):
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"""
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Test Laplacian Smoothing with uniform weights.
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Test Laplacian Smoothing with cot weights.
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"""
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meshes = TestLaplacianSmoothing.init_meshes(10, 100, 300)
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@ -161,7 +162,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
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def test_laplacian_smoothing_cotcurv(self):
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"""
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Test Laplacian Smoothing with uniform weights.
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Test Laplacian Smoothing with cotcurv weights.
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"""
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meshes = TestLaplacianSmoothing.init_meshes(10, 100, 300)
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