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252 lines
8.6 KiB
Python
252 lines
8.6 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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"""
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Some ray datastructures.
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"""
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import random
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from dataclasses import dataclass
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from typing import Dict, Optional
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import torch
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from torchtyping import TensorType
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from nerfactory.utils.math import Gaussians, conical_frustum_to_gaussian
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from nerfactory.utils.tensor_dataclass import TensorDataclass
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@dataclass
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class Frustums(TensorDataclass):
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"""Describes region of space as a frustum."""
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origins: TensorType["bs":..., 3]
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"""xyz coordinate for ray origin."""
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directions: TensorType["bs":..., 3]
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"""Direction of ray."""
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starts: TensorType["bs":..., 1]
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"""Where the frustum starts along a ray."""
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ends: TensorType["bs":..., 1]
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"""Where the frustum ends along a ray."""
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pixel_area: TensorType["bs":..., 1]
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"""Projected area of pixel a distance 1 away from origin."""
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def get_positions(self) -> TensorType[..., 3]:
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"""Calulates "center" position of frustum. Not weighted by mass.
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Returns:
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xyz positions.
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"""
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return self.origins + self.directions * (self.starts + self.ends) / 2
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def get_gaussian_blob(self) -> Gaussians:
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"""Calculates guassian approximation of conical frustum.
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Resturns:
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Conical frustums approximated by gaussian distribution.
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"""
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# Cone radius is set such that the square pixel_area matches the cone area.
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cone_radius = torch.sqrt(self.pixel_area) / 1.7724538509055159 # r = sqrt(pixel_area / pi)
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return conical_frustum_to_gaussian(
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origins=self.origins,
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directions=self.directions,
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starts=self.starts,
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ends=self.ends,
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radius=cone_radius,
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)
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@classmethod
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def get_mock_frustum(cls, device="cpu") -> "Frustums":
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"""Helper function to generate a placeholder frustum.
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Returns:
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A size 1 frustum with meaningless values.
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"""
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return Frustums(
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origins=torch.ones((1, 3)).to(device),
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directions=torch.ones((1, 3)).to(device),
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starts=torch.ones((1, 1)).to(device),
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ends=torch.ones((1, 1)).to(device),
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pixel_area=torch.ones((1, 1)).to(device),
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)
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@dataclass
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class RaySamples(TensorDataclass):
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"""Samples along a ray"""
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frustums: Frustums
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"""Frustums along ray."""
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camera_indices: Optional[TensorType["bs":..., 1]] = None
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"""Camera index."""
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valid_mask: Optional[TensorType["bs":..., 1]] = None
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"""Rays that are valid."""
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deltas: Optional[TensorType["bs":..., 1]] = None
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""""width" of each sample."""
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metadata: Optional[Dict[str, TensorType["bs":..., "latent_dims"]]] = None
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"""addtional information relevant to generating ray samples"""
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def get_weights(self, densities: TensorType[..., "num_samples", 1]) -> TensorType[..., "num_samples", 1]:
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"""Return weights based on predicted densities
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Args:
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densities: Predicted densities for samples along ray
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Returns:
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Weights for each sample
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"""
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delta_density = self.deltas * densities
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alphas = 1 - torch.exp(-delta_density)
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# TODO: put both versions of the transmittance calculation somewhere
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# mip-nerf version of transmittance calculation:
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transmittance = torch.cumsum(delta_density[..., :-1, :], dim=-2)
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transmittance = torch.cat(
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[torch.zeros((*transmittance.shape[:1], 1, 1), device=densities.device), transmittance], dim=-2
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)
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transmittance = torch.exp(-transmittance) # [..., "num_samples"]
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# most nerf codebases do the following:
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# transmittance = torch.cat(
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# [torch.ones((*alphas.shape[:1], 1)).to(densities.device), 1.0 - alphas + 1e-10], dim=-1
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# )
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# transmittance = torch.cumprod(transmittance, dim=-1)[..., :-1] # [..., "num_samples"]
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weights = alphas * transmittance # [..., "num_samples"]
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return weights
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def set_valid_mask(self, valid_mask: TensorType[..., "num_samples"]) -> None:
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"""Sets valid mask
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Args:
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valid_mask: the mask to use
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"""
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self.valid_mask = valid_mask
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def apply_masks(self) -> "RaySamples":
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"""Use valid_mask to mask samples.
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Returns:
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New set of masked samples.
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"""
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if self.valid_mask is not None:
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return self[self.valid_mask[..., 0]]
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return self
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@dataclass
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class RayBundle(TensorDataclass):
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"""A bundle of ray parameters."""
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# TODO(ethan): make sure the sizes with ... are correct
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origins: TensorType[..., 3]
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"""Ray origins (XYZ)"""
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directions: TensorType[..., 3]
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"""Unit ray direction vector"""
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pixel_area: TensorType[..., 1]
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"""Projected area of pixel a distance 1 away from origin"""
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camera_indices: Optional[TensorType[..., 1]] = None
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"""Camera indices"""
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nears: Optional[TensorType[..., 1]] = None
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"""Distance along ray to start sampling"""
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fars: Optional[TensorType[..., 1]] = None
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"""Rays Distance along ray to stop sampling"""
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valid_mask: Optional[TensorType[..., 1, bool]] = None
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"""Rays that are valid"""
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metadata: Optional[Dict[str, TensorType["num_rays", "latent_dims"]]] = None
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"""Additional metadata or data needed for interpolation, will mimic shape of rays"""
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def set_camera_indices(self, camera_index: int) -> None:
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"""Sets all of the the camera indices to a specific camera index.
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Args:
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camera_index: Camera index.
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"""
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self.camera_indices = torch.ones_like(self.origins[..., 0:1]).long() * camera_index
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def __len__(self):
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num_rays = torch.numel(self.origins) // self.origins.shape[-1]
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return num_rays
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def sample(self, num_rays: int) -> "RayBundle":
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"""Returns a RayBundle as a subset of rays.
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Args:
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num_rays: Number of rays in output RayBundle
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Returns:
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RayBundle with subset of rays.
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"""
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assert num_rays <= len(self)
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indices = random.sample(range(len(self)), k=num_rays)
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return self[indices]
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def get_row_major_sliced_ray_bundle(self, start_idx: int, end_idx: int) -> "RayBundle":
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"""Flattens RayBundle and extracts chunk given start and end indicies.
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Args:
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start_idx: Start index of RayBundle chunk.
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end_idx: End index of RayBundle chunk.
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Returns:
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Flattened RayBundle with end_idx-start_idx rays.
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"""
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return self.flatten()[start_idx:end_idx]
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def get_ray_samples(
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self, bin_starts: TensorType["bs":..., "num_samples", 1], bin_ends: TensorType["bs":..., "num_samples", 1]
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) -> RaySamples:
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"""Produces samples for each ray by projection points along the ray direction. Currently samples uniformly.
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Args:
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bin_starts: Distance from origin to start of bin.
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bin_ends: Distance from origin to end of bin.
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Returns:
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Samples projected along ray.
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"""
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device = self.origins.device
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valid_mask = torch.ones((bin_starts.shape), dtype=torch.bool, device=device)
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dists = bin_ends - bin_starts # [..., N_samples, 1]
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deltas = dists * torch.norm(self.directions[:, :], dim=-1)[..., None, None]
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if self.camera_indices is not None:
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camera_indices = self.camera_indices[..., None]
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else:
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camera_indices = None
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shaped_raybundle_fields = self[..., None]
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frustums = Frustums(
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origins=shaped_raybundle_fields.origins, # [..., 1, 3]
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directions=shaped_raybundle_fields.directions, # [..., 1, 3]
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starts=bin_starts, # [..., N_samples, 1]
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ends=bin_ends, # [..., N_samples, 1]
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pixel_area=shaped_raybundle_fields.pixel_area, # [..., 1, 1]
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).to(device)
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ray_samples = RaySamples(
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frustums=frustums,
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camera_indices=camera_indices, # [..., 1, 1]
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valid_mask=valid_mask, # [..., N_samples, 1]
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deltas=deltas, # [..., N_samples, 1]
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metadata=shaped_raybundle_fields.metadata,
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)
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return ray_samples
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