small website updates

Summary: Small fixes to website rendering

Reviewed By: jcjohnson

Differential Revision: D23281746

fbshipit-source-id: c9dc8edd5e52f39d4e0e19f10ecc7e035b39feda

docs/notes/cameras.md

@@ -1,3 +1,8 @@
+---
+hide_title: true
+sidebar_label: Cameras
+---
+
 # Cameras
 
 ## Camera Coordinate Systems
@@ -6,7 +11,7 @@ When working with 3D data, there are 4 coordinate systems users need to know
 * **World coordinate system**
 This is the system the object/scene lives - the world.
 * **Camera view coordinate system**
-This is the system that has its origin on the image plane and the `Z`-axis perpendicular to the image plane. In PyTorch3D, we assume that `+X` points left, and `+Y` points up and `+Z` points out from the image plane. The transformation from world to view happens after applying a rotation (`R`) and translation (`T`). 
+This is the system that has its origin on the image plane and the `Z`-axis perpendicular to the image plane. In PyTorch3D, we assume that `+X` points left, and `+Y` points up and `+Z` points out from the image plane. The transformation from world to view happens after applying a rotation (`R`) and translation (`T`).
 * **NDC coordinate system**
 This is the normalized coordinate system that confines in a volume the renderered part of the object/scene. Also known as view volume. Under 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 to NDC happens after applying the camera projection matrix (`P`).
 * **Screen coordinate system**
@@ -19,7 +24,7 @@ An illustration of the 4 coordinate systems is shown below
 
 Cameras in PyTorch3D transform an object/scene from world to NDC by first transforming the object/scene to view (via transforms `R` and `T`) and then projecting the 3D object/scene to NDC (via the projection matrix `P`, else known as camera matrix). Thus, the camera parameters in `P` are assumed to be in NDC space. If the user has camera parameters in screen space, which is a common use case, the parameters should transformed to NDC (see below for an example)
 
-We describe the camera types in PyTorch3D and the convention for the camera parameters provided at construction time. 
+We describe the camera types in PyTorch3D and the convention for the camera parameters provided at construction time.
 
 ### Camera Types
 
@@ -28,12 +33,12 @@ All cameras inherit from `CamerasBase` which is a base class for all cameras. Py
 * `get_world_to_view_transform` which returns a 3D transform from world coordinates to the camera view coordinates (R, T)
 * `get_full_projection_transform` which composes the projection transform (P) with the world-to-view transform (R, T)
 * `transform_points` which takes a set of input points in world coordinates and projects to NDC coordinates ranging from [-1, -1, znear] to  [+1, +1, zfar].
-* `transform_points_screen` which takes a set of input points in world coordinates and projects them to the screen coordinates ranging from [0, 0, znear] to [W-1, H-1, zfar] 
+* `transform_points_screen` which takes a set of input points in world coordinates and projects them to the screen coordinates ranging from [0, 0, znear] to [W-1, H-1, zfar]
 
 Users can easily customize their own cameras. For each new camera, users should implement the `get_projection_transform` routine that returns the mapping `P` from camera view coordinates to NDC coordinates.
 
 #### FoVPerspectiveCameras, FoVOrthographicCameras
-These two cameras follow the OpenGL convention for perspective and orthographic cameras respectively. The user provides the near `znear` and far `zfar` field which confines the view volume in the `Z` axis. The view volume in the `XY` plane is defined by field of view angle (`fov`) in the case of `FoVPerspectiveCameras` and by `min_x, min_y, max_x, max_y` in the case of `FoVOrthographicCameras`. 
+These two cameras follow the OpenGL convention for perspective and orthographic cameras respectively. The user provides the near `znear` and far `zfar` field which confines the view volume in the `Z` axis. The view volume in the `XY` plane is defined by field of view angle (`fov`) in the case of `FoVPerspectiveCameras` and by `min_x, min_y, max_x, max_y` in the case of `FoVOrthographicCameras`.
 
 #### PerspectiveCameras, OrthographicCameras
 These two cameras follow the Multi-View Geometry convention for cameras. The user provides the focal length (`fx`, `fy`) and the principal point (`px`, `py`). For example, `camera = PerspectiveCameras(focal_length=((fx, fy),), principal_point=((px, py),))`

docs/notes/cubify.md

@@ -1,3 +1,8 @@
+---
+hide_title: true
+sidebar_label: Cubify
+---
+
 # 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.

docs/notes/datasets.md

@@ -1,3 +1,8 @@
+---
+hide_title: true
+sidebar_label: Data loaders
+---
+
 # Data loaders for common 3D Datasets
 
 ### ShapetNetCore

docs/notes/renderer_getting_started.md

@@ -41,7 +41,7 @@ Rendering requires transformations between several different coordinate frames:
 
 For example, given a teapot mesh, the world coordinate frame, camera coordiante frame and image are show in the figure below. Note that the world and camera coordinate frames have the +z direction pointing in to the page.
 
-<img src="assets/world_camera_image.png" width="1000">
+<img src="assets/world_camera_image.jpg" width="1000">
 
 ---
 

website/pages/en/index.js

@@ -131,10 +131,7 @@ loss_chamfer, _ = chamfer_distance(sample_sphere, sample_test)
         <Container>
           <ol>
             <li>
-              <strong>Install PyTorch3D:</strong>
-              <p>See the instructions
-              <a href="https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md">here</a>.
-              </p>
+              <strong>Install PyTorch3D  </strong> (following the instructions <a href="https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md">here</a>)
             </li>
             <li>
               <strong>Try a few 3D operators  </strong>