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Summary: Collection of spelling things, mostly in docs / tutorials. Reviewed By: gkioxari Differential Revision: D26101323 fbshipit-source-id: 652f62bc9d71a4ff872efa21141225e43191353a
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import torch
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from pytorch3d.renderer import EmissionAbsorptionRaymarcher
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from pytorch3d.renderer.implicit.raymarching import (
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_check_density_bounds,
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_check_raymarcher_inputs,
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_shifted_cumprod,
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)
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class EmissionAbsorptionNeRFRaymarcher(EmissionAbsorptionRaymarcher):
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"""
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This is essentially the `pytorch3d.renderer.EmissionAbsorptionRaymarcher`
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which additionally returns the rendering weights. It also skips returning
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the computation of the alpha-mask which is, in case of NeRF, equal to 1
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everywhere.
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The weights are later used in the NeRF pipeline to carry out the importance
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ray-sampling for the fine rendering pass.
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For more details about the EmissionAbsorptionRaymarcher please refer to
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the documentation of `pytorch3d.renderer.EmissionAbsorptionRaymarcher`.
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"""
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def forward(
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self,
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rays_densities: torch.Tensor,
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rays_features: torch.Tensor,
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eps: float = 1e-10,
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**kwargs,
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) -> torch.Tensor:
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"""
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Args:
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rays_densities: Per-ray density values represented with a tensor
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of shape `(..., n_points_per_ray, 1)` whose values range in [0, 1].
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rays_features: Per-ray feature values represented with a tensor
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of shape `(..., n_points_per_ray, feature_dim)`.
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eps: A lower bound added to `rays_densities` before computing
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the absorption function (cumprod of `1-rays_densities` along
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each ray). This prevents the cumprod to yield exact 0
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which would inhibit any gradient-based learning.
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Returns:
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features: A tensor of shape `(..., feature_dim)` containing
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the rendered features for each ray.
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weights: A tensor of shape `(..., n_points_per_ray)` containing
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the ray-specific emission-absorption distribution.
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Each ray distribution `(..., :)` is a valid probability
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distribution, i.e. it contains non-negative values that integrate
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to 1, such that `weights.sum(dim=-1)==1).all()` yields `True`.
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"""
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_check_raymarcher_inputs(
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rays_densities,
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rays_features,
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None,
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z_can_be_none=True,
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features_can_be_none=False,
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density_1d=True,
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)
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_check_density_bounds(rays_densities)
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rays_densities = rays_densities[..., 0]
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absorption = _shifted_cumprod(
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(1.0 + eps) - rays_densities, shift=self.surface_thickness
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)
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weights = rays_densities * absorption
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features = (weights[..., None] * rays_features).sum(dim=-2)
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return features, weights
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