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64 lines
2.5 KiB
Python
64 lines
2.5 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Collection of Ray Losses"""
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import torch
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from torchtyping import TensorType
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from nerfactory.cameras.rays import RaySamples
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def distortion_loss(
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ray_samples: RaySamples,
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densities: TensorType["bs":..., "num_samples", 1] = None,
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weights: TensorType["bs":..., "num_samples", 1] = None,
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scale_factor: float = 1.0,
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) -> TensorType["bs":..., 1]:
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"""Ray baserd distortion loss proposed in MipNeRF-360. Returns distortion Loss.
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.. math::
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\\mathcal{L}(\\mathbf{s}, \\mathbf{w}) =\\iint\\limits_{-\\infty}^{\\,\\,\\,\\infty}
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\\mathbf{w}_\\mathbf{s}(u)\\mathbf{w}_\\mathbf{s}(v)|u - v|\\,d_{u}\\,d_{v}
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where :math:`\\mathbf{w}_\\mathbf{s}(u)=\\sum_i w_i \\mathbb{1}_{[\\mathbf{s}_i, \\mathbf{s}_{i+1})}(u)`
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is the weight at location :math:`u` between bin locations :math:`s_i` and :math:`s_{i+1}`.
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Args:
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ray_samples: Ray samples to compute loss over
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densities: Predicted sample densities
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weights: Predicted weights from densities and sample locations
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scale_factor: Scale the starts and ends by this factor
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"""
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if torch.is_tensor(densities):
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assert not torch.is_tensor(weights), "Cannot use both densities and weights"
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# Compute the weight at each sample location
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weights = ray_samples.get_weights(densities)
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if torch.is_tensor(weights):
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assert not torch.is_tensor(densities), "Cannot use both densities and weights"
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starts = ray_samples.frustums.starts * scale_factor
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ends = ray_samples.frustums.ends * scale_factor
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midpoints = (starts + ends) / 2.0 # (..., num_samples, 1)
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loss = (
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weights * weights[..., None, :, 0] * torch.abs(midpoints - midpoints[..., None, :, 0])
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) # (..., num_samples, num_samples)
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loss = torch.sum(loss, dim=(-1, -2))[..., None] # (..., num_samples)
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loss = loss + 1 / 3.0 * torch.sum(weights**2 * (ends - starts), dim=-2)
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return loss
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