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More renderer parameter descriptions

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Summary:
Copy some descriptions of renderer parameters to more places so they are easier to find.

Also a couple of small corrections, and make RasterizationSettings a dataclass.

Reviewed By: nikhilaravi, patricklabatut

Differential Revision: D30899822

fbshipit-source-id: 805cf366acb7d51cb308fa574deff0657c199673
This commit is contained in:
Jeremy Reizenstein 2021-09-24 09:58:10 -07:00 committed by Facebook GitHub Bot
parent 860b742a02
commit 9a737da83c
6 changed files with 114 additions and 68 deletions
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16
pytorch3d/renderer/blending.py
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@ -16,8 +16,22 @@ from pytorch3d import _C
# NOTE: All blending function should return an RGBA image per batch element
# Data class to store blending params with defaults
class BlendParams(NamedTuple):
"""
Data class to store blending params with defaults
Members:
sigma (float): Controls the width of the sigmoid function used to
calculate the 2D distance based probability. Determines the
sharpness of the edges of the shape.
Higher => faces have less defined edges.
gamma (float): Controls the scaling of the exponential function used
to set the opacity of the color.
Higher => faces are more transparent.
background_color: RGB values for the background color as a tuple or
as a tensor of three floats.
"""
sigma: float = 1e-4
gamma: float = 1e-4
background_color: Union[torch.Tensor, Sequence[float]] = (1.0, 1.0, 1.0)

7
pytorch3d/renderer/mesh/rasterize_meshes.py
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@ -73,12 +73,15 @@ def rasterize_meshes(
affect the output, but can affect the speed of the forward pass.
faces_per_bin: Only applicable when using coarse-to-fine rasterization
(bin_size > 0); this is the maximum number of faces allowed within each
bin. If more than this many faces actually fall into a bin, an error
will be raised. This should not affect the output values, but can affect
bin. This should not affect the output values, but can affect
the memory usage in the forward pass.
perspective_correct: Bool, Whether to apply perspective correction when computing
barycentric coordinates for pixels. This should be set to True if a perspective
camera is used.
clip_barycentric_coords: Whether, after any perspective correction is applied
but before the depth is calculated (e.g. for z clipping),
to "correct" a location outside the face (i.e. with a negative
barycentric coordinate) to a position on the edge of the face.
cull_backfaces: Bool, Whether to only rasterize mesh faces which are
visible to the camera. This assumes that vertices of
front-facing triangles are ordered in an anti-clockwise

97
pytorch3d/renderer/mesh/rasterizer.py
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@ -4,6 +4,7 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import NamedTuple, Optional, Tuple, Union
import torch
@ -20,46 +21,66 @@ class Fragments(NamedTuple):
dists: torch.Tensor
# Class to store the mesh rasterization params with defaults
@dataclass
class RasterizationSettings:
__slots__ = [
"image_size",
"blur_radius",
"faces_per_pixel",
"bin_size",
"max_faces_per_bin",
"perspective_correct",
"clip_barycentric_coords",
"cull_backfaces",
"z_clip_value",
"cull_to_frustum",
]
"""
Class to store the mesh rasterization params with defaults
def __init__(
self,
image_size: Union[int, Tuple[int, int]] = 256,
blur_radius: float = 0.0,
faces_per_pixel: int = 1,
bin_size: Optional[int] = None,
max_faces_per_bin: Optional[int] = None,
# set perspective_correct = None so that the
# value can be inferred correctly from the Camera type
perspective_correct: Optional[bool] = None,
clip_barycentric_coords: Optional[bool] = None,
cull_backfaces: bool = False,
z_clip_value: Optional[float] = None,
cull_to_frustum: bool = False,
) -> None:
self.image_size = image_size
self.blur_radius = blur_radius
self.faces_per_pixel = faces_per_pixel
self.bin_size = bin_size
self.max_faces_per_bin = max_faces_per_bin
self.perspective_correct = perspective_correct
self.clip_barycentric_coords = clip_barycentric_coords
self.cull_backfaces = cull_backfaces
self.z_clip_value = z_clip_value
self.cull_to_frustum = cull_to_frustum
Members:
image_size: Either common height and width or (height, width), in pixels.
blur_radius: Float distance in the range [0, 2] used to expand the face
bounding boxes for rasterization. Setting blur radius
results in blurred edges around the shape instead of a
hard boundary. Set to 0 for no blur.
faces_per_pixel: (int) Number of faces to keep track of per pixel.
We return the nearest faces_per_pixel faces along the z-axis.
bin_size: Size of bins to use for coarse-to-fine rasterization. Setting
bin_size=0 uses naive rasterization; setting bin_size=None attempts
to set it heuristically based on the shape of the input. This should
not affect the output, but can affect the speed of the forward pass.
max_faces_per_bin: Only applicable when using coarse-to-fine
rasterization (bin_size != 0); this is the maximum number of faces
allowed within each bin. This should not affect the output values,
but can affect the memory usage in the forward pass.
Setting max_faces_per_bin=None attempts to set with a heuristic.
perspective_correct: Whether to apply perspective correction when
computing barycentric coordinates for pixels.
None (default) means make correction if the camera uses perspective.
clip_barycentric_coords: Whether, after any perspective correction
is applied but before the depth is calculated (e.g. for
z clipping), to "correct" a location outside the face (i.e. with
a negative barycentric coordinate) to a position on the edge of the
face. None (default) means clip if blur_radius > 0, which is a condition
under which such outside-face-points are likely.
cull_backfaces: Whether to only rasterize mesh faces which are
visible to the camera. This assumes that vertices of
front-facing triangles are ordered in an anti-clockwise
fashion, and triangles that face away from the camera are
in a clockwise order relative to the current view
direction. NOTE: This will only work if the mesh faces are
consistently defined with counter-clockwise ordering when
viewed from the outside.
z_clip_value: if not None, then triangles will be clipped (and possibly
subdivided into smaller triangles) such that z >= z_clip_value.
This avoids camera projections that go to infinity as z->0.
Default is None as clipping affects rasterization speed and
should only be turned on if explicitly needed.
See clip.py for all the extra computation that is required.
cull_to_frustum: Whether to cull triangles outside the view frustum.
Culling involves removing all faces which fall outside view frustum.
Default is False for performance as often not needed.
"""
image_size: Union[int, Tuple[int, int]] = 256
blur_radius: float = 0.0
faces_per_pixel: int = 1
bin_size: Optional[int] = None
max_faces_per_bin: Optional[int] = None
perspective_correct: Optional[bool] = None
clip_barycentric_coords: Optional[bool] = None
cull_backfaces: bool = False
z_clip_value: Optional[float] = None
cull_to_frustum: bool = False
class MeshRasterizer(nn.Module):

3
pytorch3d/renderer/points/rasterize_points.py
View File

@ -64,8 +64,7 @@ def rasterize_points(
affect the output, but can affect the speed of the forward pass.
points_per_bin: Only applicable when using coarse-to-fine rasterization
(bin_size > 0); this is the maximum number of points allowed within each
bin. If more than this many points actually fall into a bin, an error
will be raised. This should not affect the output values, but can affect
bin. This should not affect the output values, but can affect
the memory usage in the forward pass.
Returns:

50
pytorch3d/renderer/points/rasterizer.py
View File

@ -5,7 +5,7 @@
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import NamedTuple, Optional, Tuple, Union
import torch
@ -21,29 +21,35 @@ class PointFragments(NamedTuple):
dists: torch.Tensor
# Class to store the point rasterization params with defaults
@dataclass
class PointsRasterizationSettings:
__slots__ = [
"image_size",
"radius",
"points_per_pixel",
"bin_size",
"max_points_per_bin",
]
"""
Class to store the point rasterization params with defaults
def __init__(
self,
image_size: Union[int, Tuple[int, int]] = 256,
radius: Union[float, torch.Tensor] = 0.01,
points_per_pixel: int = 8,
bin_size: Optional[int] = None,
max_points_per_bin: Optional[int] = None,
) -> None:
self.image_size = image_size
self.radius = radius
self.points_per_pixel = points_per_pixel
self.bin_size = bin_size
self.max_points_per_bin = max_points_per_bin
Members:
image_size: Either common height and width or (height, width), in pixels.
radius: The radius (in NDC units) of each disk to be rasterized.
This can either be a float in which case the same radius is used
for each point, or a torch.Tensor of shape (N, P) giving a radius
per point in the batch.
points_per_pixel: (int) Number of points to keep track of per pixel.
We return the nearest points_per_pixel points along the z-axis.
bin_size: Size of bins to use for coarse-to-fine rasterization. Setting
bin_size=0 uses naive rasterization; setting bin_size=None attempts
to set it heuristically based on the shape of the input. This should
not affect the output, but can affect the speed of the forward pass.
max_points_per_bin: Only applicable when using coarse-to-fine
rasterization (bin_size != 0); this is the maximum number of points
allowed within each bin. This should not affect the output values,
but can affect the memory usage in the forward pass.
Setting max_points_per_bin=None attempts to set with a heuristic.
"""
image_size: Union[int, Tuple[int, int]] = 256
radius: Union[float, torch.Tensor] = 0.01
points_per_pixel: int = 8
bin_size: Optional[int] = None
max_points_per_bin: Optional[int] = None
class PointsRasterizer(nn.Module):

9
pytorch3d/transforms/math.py
View File

@ -5,10 +5,11 @@
# LICENSE file in the root directory of this source tree.
import math
from typing import Tuple, Union
from typing import Tuple
import torch
DEFAULT_ACOS_BOUND = 1.0 - 1e-4
@ -27,9 +28,11 @@ def acos_linear_extrapolation(
if lower_bound <= x <= upper_bound:
acos_linear_extrapolation(x) = acos(x)
elif x <= lower_bound: # 1st order Taylor approximation
acos_linear_extrapolation(x) = acos(lower_bound) + dacos/dx(lower_bound) * (x - lower_bound)
acos_linear_extrapolation(x)
= acos(lower_bound) + dacos/dx(lower_bound) * (x - lower_bound)
else: # x >= upper_bound
acos_linear_extrapolation(x) = acos(upper_bound) + dacos/dx(upper_bound) * (x - upper_bound)
acos_linear_extrapolation(x)
= acos(upper_bound) + dacos/dx(upper_bound) * (x - upper_bound)
```
Args: