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raysampling stratified_sampling doc
Summary: Followup to D39259775 (438c194ec6
)
Reviewed By: davnov134
Differential Revision: D39271753
fbshipit-source-id: 5cf11c1210369e1762ef0d5d0c7b60336711b261
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@ -89,7 +89,7 @@ class MultinomialRaysampler(torch.nn.Module):
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max_depth: The maximum depth of a ray-point.
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n_rays_per_image: If given, this amount of rays are sampled from the grid.
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unit_directions: whether to normalize direction vectors in ray bundle.
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stratified_sampling: if set, performs stratified random sampling
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stratified_sampling: if True, performs stratified random sampling
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along the ray; otherwise takes ray points at deterministic offsets.
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"""
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super().__init__()
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@ -136,9 +136,8 @@ class MultinomialRaysampler(torch.nn.Module):
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max_depth: The maximum depth of a ray-point.
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n_rays_per_image: If given, this amount of rays are sampled from the grid.
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n_pts_per_ray: The number of points sampled along each ray.
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stratified_sampling: if set, performs stratified sampling in n_pts_per_ray
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bins for each ray; otherwise takes n_pts_per_ray deterministic points
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on each ray with uniform offsets.
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stratified_sampling: if set, overrides stratified_sampling provided
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in __init__.
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Returns:
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A named tuple RayBundle with the following fields:
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origins: A tensor of shape
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@ -296,7 +295,7 @@ class MonteCarloRaysampler(torch.nn.Module):
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min_depth: The minimum depth of each ray-point.
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max_depth: The maximum depth of each ray-point.
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unit_directions: whether to normalize direction vectors in ray bundle.
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stratified_sampling: if set, performs stratified sampling in n_pts_per_ray
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stratified_sampling: if True, performs stratified sampling in n_pts_per_ray
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bins for each ray; otherwise takes n_pts_per_ray deterministic points
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on each ray with uniform offsets.
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"""
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@ -322,9 +321,9 @@ class MonteCarloRaysampler(torch.nn.Module):
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"""
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Args:
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cameras: A batch of `batch_size` cameras from which the rays are emitted.
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stratified_sampling: if set, performs stratified sampling in n_pts_per_ray
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bins for each ray; otherwise takes n_pts_per_ray deterministic points
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on each ray with uniform offsets.
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stratified_sampling: if set, overrides stratified_sampling provided
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in __init__.
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Returns:
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A named tuple RayBundle with the following fields:
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origins: A tensor of shape
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@ -514,7 +513,7 @@ def _xy_to_ray_bundle(
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max_depth: The maximum depth of each ray-point.
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n_pts_per_ray: The number of points sampled along each ray.
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unit_directions: whether to normalize direction vectors in ray bundle.
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stratified_sampling: if set, performs stratified sampling in n_pts_per_ray
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stratified_sampling: if True, performs stratified sampling in n_pts_per_ray
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bins for each ray; otherwise takes n_pts_per_ray deterministic points
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on each ray with uniform offsets.
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"""
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@ -583,7 +582,7 @@ def _jiggle_within_stratas(bin_centers: torch.Tensor) -> torch.Tensor:
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More specifically, it replaces each point's value `z`
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with a sample from a uniform random distribution on
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`[z - delta_−, z + delta_+]`, where `delta_−` is half of the difference
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`[z - delta_-, z + delta_+]`, where `delta_-` is half of the difference
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between `z` and the previous point, and `delta_+` is half of the difference
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between the next point and `z`. For the first and last items, the
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corresponding boundary deltas are assumed zero.
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