Barycentric clipping in the renderer and flat shading

Summary:
Updates to the Renderer to enable barycentric clipping. This is important when there is blurring in the rasterization step.

Also added support for flat shading.

Reviewed By: jcjohnson

Differential Revision: D19934259

fbshipit-source-id: 036e48636cd80d28a04405d7a29fcc71a2982904

pytorch3d/renderer/blending.py

@@ -90,7 +90,9 @@ def sigmoid_alpha_blend(colors, fragments, blend_params) -> torch.Tensor:
     return torch.flip(pixel_colors, [1])
 
 
-def softmax_rgb_blend(colors, fragments, blend_params) -> torch.Tensor:
+def softmax_rgb_blend(
+    colors, fragments, blend_params, znear: float = 1.0, zfar: float = 100
+) -> torch.Tensor:
     """
     RGB and alpha channel blending to return an RGBA image based on the method
     proposed in [0]
@@ -118,13 +120,16 @@ def softmax_rgb_blend(colors, fragments, blend_params) -> torch.Tensor:
               exponential function used to control the opacity of the color.
             - background_color: (3) element list/tuple/torch.Tensor specifying
               the RGB values for the background color.
+        znear: float, near clipping plane in the z direction
+        zfar: float, far clipping plane in the z direction
 
     Returns:
         RGBA pixel_colors: (N, H, W, 4)
 
     [0] Shichen Liu et al, 'Soft Rasterizer: A Differentiable Renderer for
     Image-based 3D Reasoning'
     """
+
     N, H, W, K = fragments.pix_to_face.shape
     device = fragments.pix_to_face.device
     pix_colors = torch.ones(
@@ -140,11 +145,6 @@ def softmax_rgb_blend(colors, fragments, blend_params) -> torch.Tensor:
     delta = np.exp(1e-10 / blend_params.gamma) * 1e-10
     delta = torch.tensor(delta, device=device)
 
-    # Near and far clipping planes.
-    # TODO: add zfar/znear as input params.
-    zfar = 100.0
-    znear = 1.0
-
     # Mask for padded pixels.
     mask = fragments.pix_to_face >= 0
 
@@ -164,6 +164,7 @@ def softmax_rgb_blend(colors, fragments, blend_params) -> torch.Tensor:
     # Weights for each face. Adjust the exponential by the max z to prevent
     # overflow. zbuf shape (N, H, W, K), find max over K.
     # TODO: there may still be some instability in the exponent calculation.
+
     z_inv = (zfar - fragments.zbuf) / (zfar - znear) * mask
     z_inv_max = torch.max(z_inv, dim=-1).values[..., None]
     weights_num = prob_map * torch.exp((z_inv - z_inv_max) / blend_params.gamma)

pytorch3d/renderer/mesh/__init__.py

@@ -1,5 +1,10 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
+
+from .texturing import (  # isort:skip
+    interpolate_texture_map,
+    interpolate_vertex_colors,
+)
 from .rasterize_meshes import rasterize_meshes
 from .rasterizer import MeshRasterizer, RasterizationSettings
 from .renderer import MeshRenderer
@@ -13,10 +18,6 @@
     TexturedSoftPhongShader,
 )
 from .shading import gouraud_shading, phong_shading
-from .texturing import (  # isort: skip
-    interpolate_face_attributes,
-    interpolate_texture_map,
-    interpolate_vertex_colors,
-)
+from .utils import interpolate_face_attributes
 
 __all__ = [k for k in globals().keys() if not k.startswith("_")]

pytorch3d/renderer/mesh/renderer.py

@@ -5,6 +5,9 @@
 import torch
 import torch.nn as nn
 
+from .rasterizer import Fragments
+from .utils import _clip_barycentric_coordinates, _interpolate_zbuf
+
 # A renderer class should be initialized with a
 # function for rasterization and a function for shading.
 # The rasterizer should:
@@ -34,6 +37,34 @@ def __init__(self, rasterizer, shader):
         self.shader = shader
 
     def forward(self, meshes_world, **kwargs) -> torch.Tensor:
+        """
+        Render a batch of images from a batch of meshes by rasterizing and then shading.
+
+        NOTE: If the blur radius for rasterization is > 0.0, some pixels can have one or
+        more barycentric coordinates lying outside the range [0, 1]. For a pixel with
+        out of bounds barycentric coordinates with respect to a face f, clipping is required
+        before interpolating the texture uv coordinates and z buffer so that the colors and
+        depths are limited to the range for the corresponding face.
+        """
         fragments = self.rasterizer(meshes_world, **kwargs)
+        raster_settings = kwargs.get(
+            "raster_settings", self.rasterizer.raster_settings
+        )
+        if raster_settings.blur_radius > 0.0:
+            # TODO: potentially move barycentric clipping to the rasterizer
+            # if no downstream functions requires unclipped values.
+            # This will avoid unnecssary re-interpolation of the z buffer. 
+            clipped_bary_coords = _clip_barycentric_coordinates(
+                fragments.bary_coords
+            )
+            clipped_zbuf = _interpolate_zbuf(
+                fragments.pix_to_face, clipped_bary_coords, meshes_world
+            )
+            fragments = Fragments(
+                bary_coords=clipped_bary_coords,
+                zbuf=clipped_zbuf,
+                dists=fragments.dists,
+                pix_to_face=fragments.pix_to_face,
+            )
         images = self.shader(fragments, meshes_world, **kwargs)
         return images

pytorch3d/renderer/mesh/shader.py

@@ -270,6 +270,7 @@ def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
         cameras = kwargs.get("cameras", self.cameras)
         lights = kwargs.get("lights", self.lights)
         materials = kwargs.get("materials", self.materials)
+        blend_params = kwargs.get("blend_params", self.blend_params)
         colors = phong_shading(
             meshes=meshes,
             fragments=fragments,
@@ -278,7 +279,7 @@ def forward(self, fragments, meshes, **kwargs) -> torch.Tensor:
             cameras=cameras,
             materials=materials,
         )
-        images = softmax_rgb_blend(colors, fragments, self.blend_params)
+        images = softmax_rgb_blend(colors, fragments, blend_params)
         return images
 
 

pytorch3d/renderer/mesh/shading.py

@@ -70,8 +70,12 @@ def phong_shading(
     vertex_normals = meshes.verts_normals_packed()  # (V, 3)
     faces_verts = verts[faces]
     faces_normals = vertex_normals[faces]
-    pixel_coords = interpolate_face_attributes(fragments, faces_verts)
-    pixel_normals = interpolate_face_attributes(fragments, faces_normals)
+    pixel_coords = interpolate_face_attributes(
+        fragments.pix_to_face, fragments.bary_coords, faces_verts
+    )
+    pixel_normals = interpolate_face_attributes(
+        fragments.pix_to_face, fragments.bary_coords, faces_normals
+    )
     ambient, diffuse, specular = _apply_lighting(
         pixel_coords, pixel_normals, lights, cameras, materials
     )
@@ -122,7 +126,9 @@ def gouraud_shading(
     )
     verts_colors_shaded = vertex_colors * (ambient + diffuse) + specular
     face_colors = verts_colors_shaded[faces]
-    colors = interpolate_face_attributes(fragments, face_colors)
+    colors = interpolate_face_attributes(
+        fragments.pix_to_face, fragments.bary_coords, face_colors
+    )
     return colors
 
 

pytorch3d/renderer/mesh/texturing.py

@@ -7,75 +7,7 @@
 
 from pytorch3d.structures.textures import Textures
 
-
-def _clip_barycentric_coordinates(bary) -> torch.Tensor:
-    """
-    Args:
-        bary: barycentric coordinates of shape (...., 3) where `...` represents
-            an arbitrary number of dimensions
-
-    Returns:
-        bary: All barycentric coordinate values clipped to the range [0, 1]
-        and renormalized. The output is the same shape as the input.
-    """
-    if bary.shape[-1] != 3:
-        msg = "Expected barycentric coords to have last dim = 3; got %r"
-        raise ValueError(msg % bary.shape)
-    clipped = bary.clamp(min=0, max=1)
-    clipped_sum = torch.clamp(clipped.sum(dim=-1, keepdim=True), min=1e-5)
-    clipped = clipped / clipped_sum
-    return clipped
-
-
-def interpolate_face_attributes(
-    fragments, face_attributes: torch.Tensor, bary_clip: bool = False
-) -> torch.Tensor:
-    """
-    Interpolate arbitrary face attributes using the barycentric coordinates
-    for each pixel in the rasterized output.
-
-    Args:
-        fragments:
-            The outputs of rasterization. From this we use
-
-            - pix_to_face: LongTensor of shape (N, H, W, K) specifying the indices
-              of the faces (in the packed representation) which
-              overlap each pixel in the image.
-            - barycentric_coords: FloatTensor of shape (N, H, W, K, 3) specifying
-              the barycentric coordianates of each pixel
-              relative to the faces (in the packed
-              representation) which overlap the pixel.
-        face_attributes: packed attributes of shape (total_faces, 3, D),
-            specifying the value of the attribute for each
-            vertex in the face.
-        bary_clip: Bool to indicate if barycentric_coords should be clipped
-            before being used for interpolation.
-
-    Returns:
-        pixel_vals: tensor of shape (N, H, W, K, D) giving the interpolated
-        value of the face attribute for each pixel.
-    """
-    pix_to_face = fragments.pix_to_face
-    barycentric_coords = fragments.bary_coords
-    F, FV, D = face_attributes.shape
-    if FV != 3:
-        raise ValueError("Faces can only have three vertices; got %r" % FV)
-    N, H, W, K, _ = barycentric_coords.shape
-    if pix_to_face.shape != (N, H, W, K):
-        msg = "pix_to_face must have shape (batch_size, H, W, K); got %r"
-        raise ValueError(msg % pix_to_face.shape)
-    if bary_clip:
-        barycentric_coords = _clip_barycentric_coordinates(barycentric_coords)
-
-    # Replace empty pixels in pix_to_face with 0 in order to interpolate.
-    mask = pix_to_face == -1
-    pix_to_face = pix_to_face.clone()
-    pix_to_face[mask] = 0
-    idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
-    pixel_face_vals = face_attributes.gather(0, idx).view(N, H, W, K, 3, D)
-    pixel_vals = (barycentric_coords[..., None] * pixel_face_vals).sum(dim=-2)
-    pixel_vals[mask] = 0  # Replace masked values in output.
-    return pixel_vals
+from .utils import interpolate_face_attributes
 
 
 def interpolate_texture_map(fragments, meshes) -> torch.Tensor:
@@ -97,8 +29,8 @@ def interpolate_texture_map(fragments, meshes) -> torch.Tensor:
               relative to the faces (in the packed
               representation) which overlap the pixel.
         meshes: Meshes representing a batch of meshes. It is expected that
-                meshes has a textures attribute which is an instance of the
-                Textures class.
+            meshes has a textures attribute which is an instance of the
+            Textures class.
 
     Returns:
         texels: tensor of shape (N, H, W, K, C) giving the interpolated
@@ -114,7 +46,9 @@ def interpolate_texture_map(fragments, meshes) -> torch.Tensor:
     texture_maps = meshes.textures.maps_padded()
 
     # pixel_uvs: (N, H, W, K, 2)
-    pixel_uvs = interpolate_face_attributes(fragments, faces_verts_uvs)
+    pixel_uvs = interpolate_face_attributes(
+        fragments.pix_to_face, fragments.bary_coords, faces_verts_uvs
+    )
 
     N, H_out, W_out, K = fragments.pix_to_face.shape
     N, H_in, W_in, C = texture_maps.shape  # 3 for RGB
@@ -178,5 +112,7 @@ def interpolate_vertex_colors(fragments, meshes) -> torch.Tensor:
     vertex_textures = vertex_textures[meshes.verts_padded_to_packed_idx(), :]
     faces_packed = meshes.faces_packed()
     faces_textures = vertex_textures[faces_packed]  # (F, 3, C)
-    texels = interpolate_face_attributes(fragments, faces_textures)
+    texels = interpolate_face_attributes(
+        fragments.pix_to_face, fragments.bary_coords, faces_textures
+    )
     return texels

pytorch3d/renderer/mesh/utils.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+
+import torch
+
+
+def _clip_barycentric_coordinates(bary) -> torch.Tensor:
+    """
+    Args:
+        bary: barycentric coordinates of shape (...., 3) where `...` represents
+            an arbitrary number of dimensions
+
+    Returns:
+        bary: Barycentric coordinates clipped (i.e any values < 0 are set to 0)
+        and renormalized. We only clip  the negative values. Values > 1 will fall
+        into the [0, 1] range after renormalization.
+        The output is the same shape as the input.
+    """
+    if bary.shape[-1] != 3:
+        msg = "Expected barycentric coords to have last dim = 3; got %r"
+        raise ValueError(msg % bary.shape)
+    clipped = bary.clamp(min=0.0)
+    clipped_sum = torch.clamp(clipped.sum(dim=-1, keepdim=True), min=1e-5)
+    clipped = clipped / clipped_sum
+    return clipped
+
+
+def interpolate_face_attributes(
+    pix_to_face: torch.Tensor,
+    barycentric_coords: torch.Tensor,
+    face_attributes: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Interpolate arbitrary face attributes using the barycentric coordinates
+    for each pixel in the rasterized output.
+
+    Args:
+        pix_to_face: LongTensor of shape (N, H, W, K) specifying the indices
+            of the faces (in the packed representation) which
+            overlap each pixel in the image.
+        barycentric_coords: FloatTensor of shape (N, H, W, K, 3) specifying
+            the barycentric coordianates of each pixel
+            relative to the faces (in the packed
+            representation) which overlap the pixel.
+        face_attributes: packed attributes of shape (total_faces, 3, D),
+            specifying the value of the attribute for each
+            vertex in the face.
+
+    Returns:
+        pixel_vals: tensor of shape (N, H, W, K, D) giving the interpolated
+        value of the face attribute for each pixel.
+    """
+    F, FV, D = face_attributes.shape
+    if FV != 3:
+        raise ValueError("Faces can only have three vertices; got %r" % FV)
+    N, H, W, K, _ = barycentric_coords.shape
+    if pix_to_face.shape != (N, H, W, K):
+        msg = "pix_to_face must have shape (batch_size, H, W, K); got %r"
+        raise ValueError(msg % pix_to_face.shape)
+
+    # Replace empty pixels in pix_to_face with 0 in order to interpolate.
+    mask = pix_to_face == -1
+    pix_to_face = pix_to_face.clone()
+    pix_to_face[mask] = 0
+    idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
+    pixel_face_vals = face_attributes.gather(0, idx).view(N, H, W, K, 3, D)
+    pixel_vals = (barycentric_coords[..., None] * pixel_face_vals).sum(dim=-2)
+    pixel_vals[mask] = 0  # Replace masked values in output.
+    return pixel_vals
+
+
+def _interpolate_zbuf(
+    pix_to_face: torch.Tensor, barycentric_coords: torch.Tensor, meshes
+) -> torch.Tensor:
+    """
+    A helper function to calculate the z buffer for each pixel in the
+    rasterized output.
+
+    Args:
+        pix_to_face: LongTensor of shape (N, H, W, K) specifying the indices
+            of the faces (in the packed representation) which
+            overlap each pixel in the image.
+        barycentric_coords: FloatTensor of shape (N, H, W, K, 3) specifying
+            the barycentric coordianates of each pixel
+            relative to the faces (in the packed
+            representation) which overlap the pixel.
+        meshes: Meshes object representing a batch of meshes.
+
+    Returns:
+        zbuffer: (N, H, W, K) FloatTensor
+    """
+    verts = meshes.verts_packed()
+    faces = meshes.faces_packed()
+    faces_verts_z = verts[faces][..., 2][..., None]  # (F, 3, 1)
+    return interpolate_face_attributes(
+        pix_to_face, barycentric_coords, faces_verts_z
+    )[
+        ..., 0
+    ]  # (1, H, W, K)

tests/test_mesh_rendering_utils.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+
+import unittest
+import torch
+
+from pytorch3d.renderer.mesh.utils import _clip_barycentric_coordinates
+
+
+class TestMeshRenderingUtils(unittest.TestCase):
+    def test_bary_clip(self):
+        N = 10
+        bary = torch.randn(size=(N, 3))
+        # randomly make some values negative
+        bary[bary < 0.3] *= -1.0
+        # randomly make some values be greater than 1
+        bary[bary > 0.8] *= 2.0
+        negative_mask = bary < 0.0
+        positive_mask = bary > 1.0
+        clipped = _clip_barycentric_coordinates(bary)
+        self.assertTrue(clipped[negative_mask].sum() == 0)
+        self.assertTrue(clipped[positive_mask].gt(1.0).sum() == 0)
+        self.assertTrue(torch.allclose(clipped.sum(dim=-1), torch.ones(N)))