Allow single offset in offset_verts

Summary:
It is common when trying things out to want to move a whole mesh or point cloud by the same amount. Here we allow the offset functions to broadcast.

Also add a sanity check to join_meshes_as_scene which it is easy to call wrongly.

Reviewed By: nikhilaravi

Differential Revision: D25980593

fbshipit-source-id: cdf1568e1317e3b81ad94ed4e608ba7eef81290b

docs/tutorials/fit_textured_mesh.ipynb

@@ -255,7 +255,7 @@
     "N = verts.shape[0]\n",
     "center = verts.mean(0)\n",
     "scale = max((verts - center).abs().max(0)[0])\n",
-    "mesh.offset_verts_(-center.expand(N, 3))\n",
+    "mesh.offset_verts_(-center)\n",
     "mesh.scale_verts_((1.0 / float(scale)));"
    ]
   },

pytorch3d/structures/meshes.py

@@ -1255,12 +1255,15 @@ class Meshes(object):
         Add an offset to the vertices of this Meshes. In place operation.
 
         Args:
-            vert_offsets_packed: A Tensor of the same shape as self.verts_packed
-                               giving offsets to be added to all vertices.
+            vert_offsets_packed: A Tensor of shape (3,) or the same shape as
+                                self.verts_packed, giving offsets to be added
+                                to all vertices.
         Returns:
             self.
         """
         verts_packed = self.verts_packed()
+        if vert_offsets_packed.shape == (3,):
+            vert_offsets_packed = vert_offsets_packed.expand_as(verts_packed)
         if vert_offsets_packed.shape != verts_packed.shape:
             raise ValueError("Verts offsets must have dimension (all_v, 3).")
         # update verts packed
@@ -1581,6 +1584,12 @@ def join_meshes_as_scene(
     Returns:
         new Meshes object containing a single mesh
     """
+    if not isinstance(include_textures, (bool, int)):
+        # We want to avoid letting join_meshes_as_scene(mesh1, mesh2) silently
+        # do the wrong thing.
+        raise ValueError(
+            f"include_textures argument cannot be {type(include_textures)}"
+        )
     if isinstance(meshes, List):
         meshes = join_meshes_as_batch(meshes, include_textures=include_textures)
 

pytorch3d/structures/pointclouds.py

@@ -793,12 +793,16 @@ class Pointclouds(object):
         Translate the point clouds by an offset. In place operation.
 
         Args:
-            offsets_packed: A Tensor of the same shape as self.points_packed
-                giving offsets to be added to all points.
+            offsets_packed: A Tensor of shape (3,) or the same shape
+                as self.points_packed giving offsets to be added to
+                all points.
+
         Returns:
             self.
         """
         points_packed = self.points_packed()
+        if offsets_packed.shape == (3,):
+            offsets_packed = offsets_packed.expand_as(points_packed)
         if offsets_packed.shape != points_packed.shape:
             raise ValueError("Offsets must have dimension (all_p, 3).")
         self._points_packed = points_packed + offsets_packed

tests/test_meshes.py

@@ -1,5 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
+import itertools
 import random
 import unittest
 
@@ -445,7 +446,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
         mesh = TestMeshes.init_mesh(N, 10, 100)
         all_v = mesh.verts_packed().size(0)
         verts_per_mesh = mesh.num_verts_per_mesh()
-        for force in [0, 1]:
+        for force, deform_shape in itertools.product([0, 1], [(all_v, 3), 3]):
             if force:
                 # force mesh to have computed attributes
                 mesh._compute_packed(refresh=True)
@@ -455,7 +456,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
                 mesh._compute_face_areas_normals(refresh=True)
                 mesh._compute_vertex_normals(refresh=True)
 
-            deform = torch.rand((all_v, 3), dtype=torch.float32, device=mesh.device)
+            deform = torch.rand(deform_shape, dtype=torch.float32, device=mesh.device)
             # new meshes class to hold the deformed mesh
             new_mesh_naive = naive_offset_verts(mesh, deform)
 
@@ -465,10 +466,14 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
             verts_cumsum = torch.cumsum(verts_per_mesh, 0).tolist()
             verts_cumsum.insert(0, 0)
             for i in range(N):
+                item_offset = (
+                    deform
+                    if deform.ndim == 1
+                    else deform[verts_cumsum[i] : verts_cumsum[i + 1]]
+                )
                 self.assertClose(
                     new_mesh.verts_list()[i],
-                    mesh.verts_list()[i]
-                    + deform[verts_cumsum[i] : verts_cumsum[i + 1]],
+                    mesh.verts_list()[i] + item_offset,
                 )
                 self.assertClose(
                     new_mesh.verts_list()[i], new_mesh_naive.verts_list()[i]

tests/test_pointclouds.py

@@ -1,6 +1,6 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
-
+import itertools
 import random
 import unittest
 
@@ -516,13 +516,13 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         clouds = self.init_cloud(N, 100, 10)
         all_p = clouds.points_packed().size(0)
         points_per_cloud = clouds.num_points_per_cloud()
-        for force in (False, True):
+        for force, deform_shape in itertools.product((0, 1), [(all_p, 3), 3]):
             if force:
                 clouds._compute_packed(refresh=True)
                 clouds._compute_padded()
                 clouds.padded_to_packed_idx()
 
-            deform = torch.rand((all_p, 3), dtype=torch.float32, device=clouds.device)
+            deform = torch.rand(deform_shape, dtype=torch.float32, device=clouds.device)
             new_clouds_naive = naive_offset(clouds, deform)
 
             new_clouds = clouds.offset(deform)
@@ -530,10 +530,14 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
             points_cumsum = torch.cumsum(points_per_cloud, 0).tolist()
             points_cumsum.insert(0, 0)
             for i in range(N):
+                item_offset = (
+                    deform
+                    if deform.ndim == 1
+                    else deform[points_cumsum[i] : points_cumsum[i + 1]]
+                )
                 self.assertClose(
                     new_clouds.points_list()[i],
-                    clouds.points_list()[i]
-                    + deform[points_cumsum[i] : points_cumsum[i + 1]],
+                    clouds.points_list()[i] + item_offset,
                 )
                 self.assertClose(
                     clouds.normals_list()[i], new_clouds_naive.normals_list()[i]