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remove unused params + cubify note

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Summary:
This diff
* removes the unused compositing params
* adds a note describing cubify

Reviewed By: nikhilaravi

Differential Revision: D22426191

fbshipit-source-id: e8aa32040bb594e1dfd7d6d98e29264feefcec7c
This commit is contained in:
Georgia Gkioxari 2020-07-09 18:02:50 -07:00 committed by Facebook GitHub Bot
parent 38eadb75e2
commit 3d7dea58e1
3 changed files with 15 additions and 20 deletions
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7
docs/notes/cubify.md Normal file
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@ -0,0 +1,7 @@
# Cubify
The [cubify operator](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/ops/cubify.py) converts an 3D occupancy grid of shape `BxDxHxW`, where `B` is the batch size, into a mesh instantiated as a [Meshes](https://github.com/facebookresearch/pytorch3d/blob/master/pytorch3d/structures/meshes.py) data structure of `B` elements. The operator replaces every occupied voxel (if its occupancy probability is greater than a user defined threshold) with a cuboid of 12 faces and 8 vertices. Shared vertices are merged, and internal faces are removed resulting in a **watertight** mesh.
The operator provides three alignment modes {*topleft*, *corner*, *center*} which define the span of the mesh vertices with respect to the voxel grid. The alignment modes are described in the figure below for a 2D grid.
![input](https://user-images.githubusercontent.com/4369065/81032959-af697380-8e46-11ea-91a8-fae89597f988.png)

11
pytorch3d/renderer/compositing.py
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@ -14,11 +14,6 @@ from pytorch3d import _C
# This can be an image (C=3) or a set of features.
# Data class to store blending params with defaults
class CompositeParams(NamedTuple):
radius: float = 4.0 / 256.0
class _CompositeAlphaPoints(torch.autograd.Function):
"""
Composite features within a z-buffer using alpha compositing. Given a z-buffer
@ -67,7 +62,7 @@ class _CompositeAlphaPoints(torch.autograd.Function):
return grad_features, grad_alphas, grad_points_idx, None
def alpha_composite(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tensor:
def alpha_composite(pointsidx, alphas, pt_clds) -> torch.Tensor:
"""
Composite features within a z-buffer using alpha compositing. Given a z-buffer
with corresponding features and weights, these values are accumulated according
@ -147,7 +142,7 @@ class _CompositeNormWeightedSumPoints(torch.autograd.Function):
return grad_features, grad_alphas, grad_points_idx, None
def norm_weighted_sum(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tensor:
def norm_weighted_sum(pointsidx, alphas, pt_clds) -> torch.Tensor:
"""
Composite features within a z-buffer using normalized weighted sum. Given a z-buffer
with corresponding features and weights, these values are accumulated
@ -226,7 +221,7 @@ class _CompositeWeightedSumPoints(torch.autograd.Function):
return grad_features, grad_alphas, grad_points_idx, None
def weighted_sum(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tensor:
def weighted_sum(pointsidx, alphas, pt_clds) -> torch.Tensor:
"""
Composite features within a z-buffer using normalized weighted sum.

17
pytorch3d/renderer/points/compositor.py
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@ -3,7 +3,7 @@
import torch
import torch.nn as nn
from ..compositing import CompositeParams, alpha_composite, norm_weighted_sum
from ..compositing import alpha_composite, norm_weighted_sum
# A compositor should take as input 3D points and some corresponding information.
@ -16,15 +16,11 @@ class AlphaCompositor(nn.Module):
Accumulate points using alpha compositing.
"""
def __init__(self, composite_params=None):
def __init__(self):
super().__init__()
self.composite_params = (
composite_params if composite_params is not None else CompositeParams()
)
def forward(self, fragments, alphas, ptclds, **kwargs) -> torch.Tensor:
images = alpha_composite(fragments, alphas, ptclds, self.composite_params)
images = alpha_composite(fragments, alphas, ptclds)
return images
@ -33,12 +29,9 @@ class NormWeightedCompositor(nn.Module):
Accumulate points using a normalized weighted sum.
"""
def __init__(self, composite_params=None):
def __init__(self):
super().__init__()
self.composite_params = (
composite_params if composite_params is not None else CompositeParams()
)
def forward(self, fragments, alphas, ptclds, **kwargs) -> torch.Tensor:
images = norm_weighted_sum(fragments, alphas, ptclds, self.composite_params)
images = norm_weighted_sum(fragments, alphas, ptclds)
return images