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point mesh distances
Summary: Implementation of point to mesh distances. The current diff contains two types: (a) Point to Edge (b) Point to Face ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- POINT_MESH_EDGE_4_100_300_5000_cuda:0 2745 3138 183 POINT_MESH_EDGE_4_100_300_10000_cuda:0 4408 4499 114 POINT_MESH_EDGE_4_100_3000_5000_cuda:0 4978 5070 101 POINT_MESH_EDGE_4_100_3000_10000_cuda:0 9076 9187 56 POINT_MESH_EDGE_4_1000_300_5000_cuda:0 1411 1487 355 POINT_MESH_EDGE_4_1000_300_10000_cuda:0 4829 5030 104 POINT_MESH_EDGE_4_1000_3000_5000_cuda:0 7539 7620 67 POINT_MESH_EDGE_4_1000_3000_10000_cuda:0 12088 12272 42 POINT_MESH_EDGE_8_100_300_5000_cuda:0 3106 3222 161 POINT_MESH_EDGE_8_100_300_10000_cuda:0 8561 8648 59 POINT_MESH_EDGE_8_100_3000_5000_cuda:0 6932 7021 73 POINT_MESH_EDGE_8_100_3000_10000_cuda:0 24032 24176 21 POINT_MESH_EDGE_8_1000_300_5000_cuda:0 5272 5399 95 POINT_MESH_EDGE_8_1000_300_10000_cuda:0 11348 11430 45 POINT_MESH_EDGE_8_1000_3000_5000_cuda:0 17478 17683 29 POINT_MESH_EDGE_8_1000_3000_10000_cuda:0 25961 26236 20 POINT_MESH_EDGE_16_100_300_5000_cuda:0 8244 8323 61 POINT_MESH_EDGE_16_100_300_10000_cuda:0 18018 18071 28 POINT_MESH_EDGE_16_100_3000_5000_cuda:0 19428 19544 26 POINT_MESH_EDGE_16_100_3000_10000_cuda:0 44967 45135 12 POINT_MESH_EDGE_16_1000_300_5000_cuda:0 7825 7937 64 POINT_MESH_EDGE_16_1000_300_10000_cuda:0 18504 18571 28 POINT_MESH_EDGE_16_1000_3000_5000_cuda:0 65805 66132 8 POINT_MESH_EDGE_16_1000_3000_10000_cuda:0 90885 91089 6 -------------------------------------------------------------------------------- Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- POINT_MESH_FACE_4_100_300_5000_cuda:0 1561 1685 321 POINT_MESH_FACE_4_100_300_10000_cuda:0 2818 2954 178 POINT_MESH_FACE_4_100_3000_5000_cuda:0 15893 16018 32 POINT_MESH_FACE_4_100_3000_10000_cuda:0 16350 16439 31 POINT_MESH_FACE_4_1000_300_5000_cuda:0 3179 3278 158 POINT_MESH_FACE_4_1000_300_10000_cuda:0 2353 2436 213 POINT_MESH_FACE_4_1000_3000_5000_cuda:0 16262 16336 31 POINT_MESH_FACE_4_1000_3000_10000_cuda:0 9334 9448 54 POINT_MESH_FACE_8_100_300_5000_cuda:0 4377 4493 115 POINT_MESH_FACE_8_100_300_10000_cuda:0 9728 9822 52 POINT_MESH_FACE_8_100_3000_5000_cuda:0 26428 26544 19 POINT_MESH_FACE_8_100_3000_10000_cuda:0 42238 43031 12 POINT_MESH_FACE_8_1000_300_5000_cuda:0 3891 3982 129 POINT_MESH_FACE_8_1000_300_10000_cuda:0 5363 5429 94 POINT_MESH_FACE_8_1000_3000_5000_cuda:0 20998 21084 24 POINT_MESH_FACE_8_1000_3000_10000_cuda:0 39711 39897 13 POINT_MESH_FACE_16_100_300_5000_cuda:0 5955 6001 84 POINT_MESH_FACE_16_100_300_10000_cuda:0 12082 12144 42 POINT_MESH_FACE_16_100_3000_5000_cuda:0 44996 45176 12 POINT_MESH_FACE_16_100_3000_10000_cuda:0 73042 73197 7 POINT_MESH_FACE_16_1000_300_5000_cuda:0 8292 8374 61 POINT_MESH_FACE_16_1000_300_10000_cuda:0 19442 19506 26 POINT_MESH_FACE_16_1000_3000_5000_cuda:0 36059 36194 14 POINT_MESH_FACE_16_1000_3000_10000_cuda:0 64644 64822 8 -------------------------------------------------------------------------------- ``` Reviewed By: jcjohnson Differential Revision: D20590462 fbshipit-source-id: 42a39837b514a546ac9471bfaff60eefe7fae829
This commit is contained in:
Georgia Gkioxari
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Facebook GitHub Bot
Facebook GitHub Bot
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474c8b456a
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487d4d6607
@@ -147,36 +147,38 @@ class Meshes(object):
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Total number of unique edges = sum(E_n)
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# SPHINX IGNORE
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Name | Size | Example from above
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------------------------------|-------------------------|----------------------
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| |
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edges_packed | size = (sum(E_n), 2) | tensor([
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| | [0, 1],
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| | [0, 2],
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| | [1, 2],
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| | ...
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| | [10, 11],
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| | )]
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| | size = (18, 2)
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| |
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num_edges_per_mesh | size = (N) | tensor([3, 5, 10])
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| | size = (3)
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| |
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edges_packed_to_mesh_idx | size = (sum(E_n)) | tensor([
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| | 0, 0, 0,
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| | . . .
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| | 2, 2, 2
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| | ])
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| | size = (18)
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| |
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faces_packed_to_edges_packed | size = (sum(F_n), 3) | tensor([
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| | [2, 1, 0],
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| | [5, 4, 3],
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| | . . .
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| | [12, 14, 16],
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| | ])
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| | size = (10, 3)
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| |
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Name | Size | Example from above
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-------------------------------|-------------------------|----------------------
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| |
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edges_packed | size = (sum(E_n), 2) | tensor([
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| | [0, 1],
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| | [0, 2],
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| | [1, 2],
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| | ...
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| | [10, 11],
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| | )]
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| | size = (18, 2)
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| |
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num_edges_per_mesh | size = (N) | tensor([3, 5, 10])
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| | size = (3)
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| |
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edges_packed_to_mesh_idx | size = (sum(E_n)) | tensor([
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| | 0, 0, 0,
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| | . . .
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| | 2, 2, 2
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| | ])
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| | size = (18)
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| |
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faces_packed_to_edges_packed | size = (sum(F_n), 3) | tensor([
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| | [2, 1, 0],
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| | [5, 4, 3],
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| | . . .
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| | [12, 14, 16],
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| | ])
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| | size = (10, 3)
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| |
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mesh_to_edges_packed_first_idx | size = (N) | tensor([0, 3, 8])
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| | size = (3)
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----------------------------------------------------------------------------
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# SPHINX IGNORE
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"""
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@@ -197,6 +199,7 @@ class Meshes(object):
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"_num_faces_per_mesh",
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"_edges_packed",
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"_edges_packed_to_mesh_idx",
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"_mesh_to_edges_packed_first_idx",
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"_faces_packed_to_edges_packed",
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"_num_edges_per_mesh",
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"_verts_padded_to_packed_idx",
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@@ -278,6 +281,7 @@ class Meshes(object):
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# Map from packed edges to corresponding mesh index.
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self._edges_packed_to_mesh_idx = None # sum(E_n)
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self._num_edges_per_mesh = None # N
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self._mesh_to_edges_packed_first_idx = None # N
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# Map from packed faces to packed edges. This represents the index of
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# the edge opposite the vertex for each vertex in the face. E.g.
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@@ -611,6 +615,17 @@ class Meshes(object):
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self._compute_edges_packed()
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return self._edges_packed_to_mesh_idx
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def mesh_to_edges_packed_first_idx(self):
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"""
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Return a 1D tensor x with length equal to the number of meshes such that
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the first edge of the ith mesh is edges_packed[x[i]].
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Returns:
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1D tensor of indices of first items.
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"""
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self._compute_edges_packed()
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return self._mesh_to_edges_packed_first_idx
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def faces_packed_to_edges_packed(self):
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"""
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Get the packed representation of the faces in terms of edges.
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@@ -955,6 +970,7 @@ class Meshes(object):
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self._faces_packed_to_mesh_idx,
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self._edges_packed_to_mesh_idx,
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self._num_edges_per_mesh,
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self._mesh_to_edges_packed_first_idx,
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]
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)
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):
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@@ -1023,13 +1039,24 @@ class Meshes(object):
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face_to_edge = inverse_idxs[face_to_edge]
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self._faces_packed_to_edges_packed = face_to_edge
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# Compute number of edges per mesh
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num_edges_per_mesh = torch.zeros(self._N, dtype=torch.int32, device=self.device)
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ones = torch.ones(1, dtype=torch.int32, device=self.device).expand(
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self._edges_packed_to_mesh_idx.shape
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)
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self._num_edges_per_mesh = num_edges_per_mesh.scatter_add(
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num_edges_per_mesh = num_edges_per_mesh.scatter_add_(
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0, self._edges_packed_to_mesh_idx, ones
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)
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self._num_edges_per_mesh = num_edges_per_mesh
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# Compute first idx for each mesh in edges_packed
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mesh_to_edges_packed_first_idx = torch.zeros(
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self._N, dtype=torch.int64, device=self.device
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)
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num_edges_cumsum = num_edges_per_mesh.cumsum(dim=0)
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mesh_to_edges_packed_first_idx[1:] = num_edges_cumsum[:-1].clone()
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self._mesh_to_edges_packed_first_idx = mesh_to_edges_packed_first_idx
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def _compute_laplacian_packed(self, refresh: bool = False):
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"""
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@@ -963,3 +963,44 @@ class Pointclouds(object):
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new._features_list = None
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new._features_packed = None
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return new
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def inside_box(self, box):
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"""
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Finds the points inside a 3D box.
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Args:
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box: FloatTensor of shape (2, 3) or (N, 2, 3) where N is the number
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of clouds.
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box[..., 0, :] gives the min x, y & z.
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box[..., 1, :] gives the max x, y & z.
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Returns:
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idx: BoolTensor of length sum(P_i) indicating whether the packed points are within the input box.
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"""
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if box.dim() > 3 or box.dim() < 2:
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raise ValueError("Input box must be of shape (2, 3) or (N, 2, 3).")
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if box.dim() == 3 and box.shape[0] != 1 and box.shape[0] != self._N:
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raise ValueError(
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"Input box dimension is incompatible with pointcloud size."
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)
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if box.dim() == 2:
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box = box[None]
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if (box[..., 0, :] > box[..., 1, :]).any():
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raise ValueError("Input box is invalid: min values larger than max values.")
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points_packed = self.points_packed()
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sumP = points_packed.shape[0]
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if box.shape[0] == 1:
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box = box.expand(sumP, 2, 3)
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elif box.shape[0] == self._N:
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box = box.unbind(0)
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box = [
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b.expand(p, 2, 3) for (b, p) in zip(box, self.num_points_per_cloud())
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]
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box = torch.cat(box, 0)
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idx = (points_packed >= box[:, 0]) * (points_packed <= box[:, 1])
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return idx
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