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https://github.com/facebookresearch/pytorch3d.git
synced 2025-12-19 22:00:35 +08:00
back face culling in rasterization
Summary: Added backface culling as an option to the `raster_settings`. This is needed for the full forward rendering of shapenet meshes with texture (some meshes contain multiple overlapping segments which have different textures). For a triangle (v0, v1, v2) define the vectors A = (v1 - v0) and B = (v2 − v0) and use this to calculate the area of the triangle as: ``` area = 0.5 * A x B area = 0.5 * ((x1 − x0)(y2 − y0) − (x2 − x0)(y1 − y0)) ``` The area will be positive if (v0, v1, v2) are oriented counterclockwise (a front face), and negative if (v0, v1, v2) are oriented clockwise (a back face). We can reuse the `edge_function` as it already calculates the triangle area. Reviewed By: jcjohnson Differential Revision: D20960115 fbshipit-source-id: 2d8a4b9ccfb653df18e79aed8d05c7ec0f057ab1
This commit is contained in:
Nikhila Ravi
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
3c6f9220fc
commit
4bf30593ff
@@ -21,6 +21,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
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self._simple_blurry_raster(rasterize_meshes_python, device, bin_size=-1)
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self._test_behind_camera(rasterize_meshes_python, device, bin_size=-1)
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self._test_perspective_correct(rasterize_meshes_python, device, bin_size=-1)
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self._test_back_face_culling(rasterize_meshes_python, device, bin_size=-1)
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def test_simple_cpu_naive(self):
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device = torch.device("cpu")
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@@ -28,6 +29,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
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self._simple_blurry_raster(rasterize_meshes, device, bin_size=0)
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self._test_behind_camera(rasterize_meshes, device, bin_size=0)
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self._test_perspective_correct(rasterize_meshes, device, bin_size=0)
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self._test_back_face_culling(rasterize_meshes, device, bin_size=0)
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def test_simple_cuda_naive(self):
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device = torch.device("cuda:0")
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@@ -35,6 +37,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
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self._simple_blurry_raster(rasterize_meshes, device, bin_size=0)
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self._test_behind_camera(rasterize_meshes, device, bin_size=0)
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self._test_perspective_correct(rasterize_meshes, device, bin_size=0)
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self._test_back_face_culling(rasterize_meshes, device, bin_size=0)
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def test_simple_cuda_binned(self):
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device = torch.device("cuda:0")
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@@ -42,6 +45,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
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self._simple_blurry_raster(rasterize_meshes, device, bin_size=5)
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self._test_behind_camera(rasterize_meshes, device, bin_size=5)
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self._test_perspective_correct(rasterize_meshes, device, bin_size=5)
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self._test_back_face_culling(rasterize_meshes, device, bin_size=5)
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def test_python_vs_cpu_vs_cuda(self):
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torch.manual_seed(231)
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@@ -377,6 +381,81 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
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args = ()
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self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
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def _test_back_face_culling(self, rasterize_meshes_fn, device, bin_size):
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# Square based pyramid mesh.
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# fmt: off
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verts = torch.tensor([
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[-0.5, 0.0, 0.5], # noqa: E241 E201 Front right
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[ 0.5, 0.0, 0.5], # noqa: E241 E201 Front left
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[ 0.5, 0.0, 1.5], # noqa: E241 E201 Back left
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[-0.5, 0.0, 1.5], # noqa: E241 E201 Back right
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[ 0.0, 1.0, 1.0] # noqa: E241 E201 Top point of pyramid
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], dtype=torch.float32, device=device)
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faces = torch.tensor([
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[2, 1, 0], # noqa: E241 E201 Square base
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[3, 2, 0], # noqa: E241 E201 Square base
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[1, 0, 4], # noqa: E241 E201 Triangle on front
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[2, 4, 3], # noqa: E241 E201 Triangle on back
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[3, 4, 0], # noqa: E241 E201 Triangle on left side
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[1, 4, 2] # noqa: E241 E201 Triangle on right side
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], dtype=torch.int64, device=device)
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# fmt: on
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mesh = Meshes(verts=[verts], faces=[faces])
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kwargs = {
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"meshes": mesh,
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"image_size": 10,
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"faces_per_pixel": 2,
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"blur_radius": 0.0,
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"perspective_correct": False,
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"cull_backfaces": False,
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}
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if bin_size != -1:
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kwargs["bin_size"] = bin_size
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# fmt: off
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pix_to_face_frontface = torch.tensor([
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, 2, 2, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, 2, 2, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241 E201
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], dtype=torch.int64, device=device)
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pix_to_face_backface = torch.tensor([
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, 3, 3, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, 3, 3, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, 3, 3, 3, 3, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, 3, 3, 3, 3, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241 E201
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241 E201
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], dtype=torch.int64, device=device)
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# fmt: on
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pix_to_face_padded = -torch.ones_like(pix_to_face_frontface)
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# Run with and without culling
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# Without culling, for k=0, the front face (i.e. face 2) is
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# rasterized and for k=1, the back face (i.e. face 3) is
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# rasterized.
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idx_f, zbuf_f, bary_f, dists_f = rasterize_meshes_fn(**kwargs)
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self.assertTrue(torch.all(idx_f[..., 0].squeeze() == pix_to_face_frontface))
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self.assertTrue(torch.all(idx_f[..., 1].squeeze() == pix_to_face_backface))
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# With culling, for k=0, the front face (i.e. face 2) is
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# rasterized and for k=1, there are no faces rasterized
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kwargs["cull_backfaces"] = True
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idx_t, zbuf_t, bary_t, dists_t = rasterize_meshes_fn(**kwargs)
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self.assertTrue(torch.all(idx_t[..., 0].squeeze() == pix_to_face_frontface))
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self.assertTrue(torch.all(idx_t[..., 1].squeeze() == pix_to_face_padded))
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def _compare_impls(
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self,
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fn1,
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