remove unused params + cubify note

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

Reviewed By: nikhilaravi

Differential Revision: D22426191

fbshipit-source-id: e8aa32040bb594e1dfd7d6d98e29264feefcec7c

Author: gkioxari

docs/notes/cubify.md

@@ -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)

pytorch3d/renderer/compositing.py

@@ -14,11 +14,6 @@
 # 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 @@ def backward(ctx, grad_output):
         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 @@ def backward(ctx, grad_output):
         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 @@ def backward(ctx, grad_output):
         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.
 

pytorch3d/renderer/points/compositor.py

@@ -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