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NDC doc fix (#28)

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Summary:
- Added clarifications about NDC coordinate system for square and non-square images.

Pull Request resolved: https://github.com/fairinternal/pytorch3d/pull/28

Reviewed By: nikhilaravi

Differential Revision: D31681444

Pulled By: bottler

fbshipit-source-id: f71eabe9b3dd54b9372cef617e08f837f316555b
This commit is contained in:
Ignacio Rocco
2021-10-17 07:41:11 -07:00
committed by Facebook GitHub Bot
parent 14dd2611ee
commit 16ebf54e69
2 changed files with 6 additions and 3 deletions
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7
pytorch3d/renderer/cameras.py
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@@ -36,10 +36,12 @@ class CamerasBase(TensorProperties):
and translation (T)
- NDC coordinate system: This is the normalized coordinate system that confines
in a volume the rendered part of the object or scene. Also known as view volume.
Given the PyTorch3D convention, (+1, +1, znear) is the top left near corner,
For square images, given the PyTorch3D convention, (+1, +1, znear) is the top left near corner,
and (-1, -1, zfar) is the bottom right far corner of the volume.
The transformation from view --> NDC happens after applying the camera
projection matrix (P) if defined in NDC space.
For non square images, we scale the points such that smallest side
has range [-1, 1] and the largest side has range [-u, u], with u > 1.
- Screen coordinate system: This is another representation of the view volume with
the XY coordinates defined in image space instead of a normalized space.
@@ -243,7 +245,8 @@ class CamerasBase(TensorProperties):
Returns the transform from camera projection space (screen or NDC) to NDC space.
For cameras that can be specified in screen space, this transform
allows points to be converted from screen to NDC space.
The default transform scales the points from [0, W-1]x[0, H-1] to [-1, 1].
The default transform scales the points from [0, W-1]x[0, H-1]
to [-1, 1]x[-u, u] or [-u, u]x[-1, 1] where u > 1 is the aspect ratio of the image.
This function should be modified per camera definitions if need be,
e.g. for Perspective/Orthographic cameras we provide a custom implementation.
This transform assumes PyTorch3D coordinate system conventions for