Source code for nerfstudio.field_components.spatial_distortions

# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
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"""Space distortions."""

import abc
from typing import Optional, Union

import torch
from functorch import jacrev, vmap
from jaxtyping import Float
from torch import Tensor, nn

from nerfstudio.utils.math import Gaussians

[docs]class SpatialDistortion(nn.Module): """Apply spatial distortions"""
[docs] @abc.abstractmethod def forward(self, positions: Union[Float[Tensor, "*bs 3"], Gaussians]) -> Union[Float[Tensor, "*bs 3"], Gaussians]: """ Args: positions: Sample to distort Returns: Union: distorted sample """
[docs]class SceneContraction(SpatialDistortion): """Contract unbounded space using the contraction was proposed in MipNeRF-360. We use the following contraction equation: .. math:: f(x) = \\begin{cases} x & ||x|| \\leq 1 \\\\ (2 - \\frac{1}{||x||})(\\frac{x}{||x||}) & ||x|| > 1 \\end{cases} If the order is not specified, we use the Frobenius norm, this will contract the space to a sphere of radius 2. If the order is L_inf (order=float("inf")), we will contract the space to a cube of side length 4. If using voxel based encodings such as the Hash encoder, we recommend using the L_inf norm. Args: order: Order of the norm. Default to the Frobenius norm. Must be set to None for Gaussians. """ def __init__(self, order: Optional[Union[float, int]] = None) -> None: super().__init__() self.order = order
[docs] def forward(self, positions): def contract(x): mag = torch.linalg.norm(x, ord=self.order, dim=-1)[..., None] return torch.where(mag < 1, x, (2 - (1 / mag)) * (x / mag)) if isinstance(positions, Gaussians): means = contract(positions.mean.clone()) def contract_gauss(x): return (2 - 1 / torch.linalg.norm(x, ord=self.order, dim=-1, keepdim=True)) * ( x / torch.linalg.norm(x, ord=self.order, dim=-1, keepdim=True) ) jc_means = vmap(jacrev(contract_gauss))(positions.mean.view(-1, positions.mean.shape[-1])) jc_means = jc_means.view(list(positions.mean.shape) + [positions.mean.shape[-1]]) # Only update covariances on positions outside the unit sphere mag = positions.mean.norm(dim=-1) mask = mag >= 1 cov = positions.cov.clone() cov[mask] = jc_means[mask] @ positions.cov[mask] @ torch.transpose(jc_means[mask], -2, -1) return Gaussians(mean=means, cov=cov) return contract(positions)