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92 lines
3.2 KiB
Python
92 lines
3.2 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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"""Space distortions."""
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from typing import Optional, Union
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import torch
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from functorch import jacrev, vmap
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from torch import nn
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from torchtyping import TensorType
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from nerfactory.utils.math import Gaussians
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class SpatialDistortion(nn.Module):
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"""Apply spatial distortions"""
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def forward(
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self, positions: Union[TensorType["bs":..., 3], Gaussians]
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) -> Union[TensorType["bs":..., 3], Gaussians]:
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"""
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Args:
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positions: Sample to distort
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Returns:
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Union: distorted sample
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"""
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class SceneContraction(SpatialDistortion):
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"""Contract unbounded space using the contraction was proposed in MipNeRF-360.
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We use the following contraction equation:
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.. math::
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f(x) = \\begin{cases}
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x & ||x|| \\leq 1 \\\\
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(2 - \\frac{1}{||x||})(\\frac{x}{||x||}) & ||x|| > 1
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\\end{cases}
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If the order is not specified, we use the Frobenius norm, this will contract the space to a sphere of
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radius 1. If the order is L_inf (order=float("inf")), we will contract the space to a cube of side length 2.
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If using voxel based encodings such as the Hash encoder, we recommend using the L_inf norm.
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Args:
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order: Order of the norm. Default to the Frobenius norm. Must be set to None for Gaussians.
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"""
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def __init__(self, order: Optional[Union[float, int]] = None) -> None:
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super().__init__()
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self.order = order
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def forward(self, positions):
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def contract(x):
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mag = torch.linalg.norm(x, ord=self.order, dim=-1)
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mask = mag >= 1
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x[mask] = (2 - (1 / mag[mask][..., None])) * (x[mask] / mag[mask][..., None])
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return x
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if isinstance(positions, Gaussians):
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means = contract(positions.mean.clone())
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contract = lambda x: (2 - (1 / torch.linalg.norm(x, ord=self.order, dim=-1, keepdim=True))) * (
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x / torch.linalg.norm(x, ord=self.order, dim=-1, keepdim=True)
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)
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jc_means = vmap(jacrev(contract))(positions.mean.view(-1, positions.mean.shape[-1]))
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jc_means = jc_means.view(list(positions.mean.shape) + [positions.mean.shape[-1]])
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# Only update covariances on positions outside the unit sphere
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mag = positions.mean.norm(dim=-1)
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mask = mag >= 1
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cov = positions.cov.clone()
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cov[mask] = jc_means[mask] @ positions.cov[mask] @ torch.transpose(jc_means[mask], -2, -1)
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return Gaussians(mean=means, cov=cov)
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return contract(positions)
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