在Google Colab中运行# Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved.
本教程展示了如何使用可微隐式函数渲染，给定场景的一组视图来拟合神经辐射场。
更具体地说，本教程将解释如何
请注意，所呈现的隐式模型是NeRF的简化版本
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng: NeRF: 将场景表示为神经辐射场以进行视图合成，ECCV 2020。
简化包括
确保已安装torch和torchvision。如果未安装pytorch3d，请使用以下单元格安装它
import os
import sys
import torch
need_pytorch3d=False
try:
import pytorch3d
except ModuleNotFoundError:
need_pytorch3d=True
if need_pytorch3d:
if torch.__version__.startswith("2.2.") and sys.platform.startswith("linux"):
# We try to install PyTorch3D via a released wheel.
pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
version_str="".join([
f"py3{sys.version_info.minor}_cu",
torch.version.cuda.replace(".",""),
f"_pyt{pyt_version_str}"
])
!pip install fvcore iopath
!pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
else:
# We try to install PyTorch3D from source.
!pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
# %matplotlib inline
# %matplotlib notebook
import os
import sys
import time
import json
import glob
import torch
import math
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from IPython import display
from tqdm.notebook import tqdm
# Data structures and functions for rendering
from pytorch3d.structures import Volumes
from pytorch3d.transforms import so3_exp_map
from pytorch3d.renderer import (
FoVPerspectiveCameras,
NDCMultinomialRaysampler,
MonteCarloRaysampler,
EmissionAbsorptionRaymarcher,
ImplicitRenderer,
RayBundle,
ray_bundle_to_ray_points,
)
# obtain the utilized device
if torch.cuda.is_available():
device = torch.device("cuda:0")
torch.cuda.set_device(device)
else:
print(
'Please note that NeRF is a resource-demanding method.'
+ ' Running this notebook on CPU will be extremely slow.'
+ ' We recommend running the example on a GPU'
+ ' with at least 10 GB of memory.'
)
device = torch.device("cpu")
!wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/plot_image_grid.py
!wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/docs/tutorials/utils/generate_cow_renders.py
from plot_image_grid import image_grid
from generate_cow_renders import generate_cow_renders
或者，如果在本地运行，请取消注释并运行以下单元格
# from utils.generate_cow_renders import generate_cow_renders
# from utils import image_grid
以下单元格生成我们的训练数据。它从多个视点渲染fit_textured_mesh.ipynb教程中的奶牛网格，并返回
注意：出于本教程的目的，本教程旨在解释隐式渲染的细节，我们不会解释在generate_cow_renders函数中实现的网格渲染的工作原理。有关网格渲染的详细说明，请参阅fit_textured_mesh.ipynb。
target_cameras, target_images, target_silhouettes = generate_cow_renders(num_views=40, azimuth_range=180)
print(f'Generated {len(target_images)} images/silhouettes/cameras.')
以下初始化一个隐式渲染器，该渲染器从目标图像的每个像素发出光线，并沿光线采样一组均匀间隔的点。在每个光线点，通过查询场景的神经模型中相应的位置来获得相应的密度和颜色值（模型在后面的单元格中进行了描述和实例化）。
渲染器由光线步进器和光线采样器组成。
MonteCarloRaysampler用于从图像平面的像素的随机子集中生成光线。像素的随机子采样在训练期间进行，以减少隐式模型的内存消耗。NDCMultinomialRaysampler遵循标准的PyTorch3D坐标网格约定（+X从右到左；+Y从下到上；+Z远离用户）。结合场景的隐式模型，NDCMultinomialRaysampler消耗大量内存，因此仅在测试时用于可视化训练结果。EmissionAbsorptionRaymarcher，它实现了标准的发射吸收光线步进算法。# render_size describes the size of both sides of the
# rendered images in pixels. Since an advantage of
# Neural Radiance Fields are high quality renders
# with a significant amount of details, we render
# the implicit function at double the size of
# target images.
render_size = target_images.shape[1] * 2
# Our rendered scene is centered around (0,0,0)
# and is enclosed inside a bounding box
# whose side is roughly equal to 3.0 (world units).
volume_extent_world = 3.0
# 1) Instantiate the raysamplers.
# Here, NDCMultinomialRaysampler generates a rectangular image
# grid of rays whose coordinates follow the PyTorch3D
# coordinate conventions.
raysampler_grid = NDCMultinomialRaysampler(
image_height=render_size,
image_width=render_size,
n_pts_per_ray=128,
min_depth=0.1,
max_depth=volume_extent_world,
)
# MonteCarloRaysampler generates a random subset
# of `n_rays_per_image` rays emitted from the image plane.
raysampler_mc = MonteCarloRaysampler(
min_x = -1.0,
max_x = 1.0,
min_y = -1.0,
max_y = 1.0,
n_rays_per_image=750,
n_pts_per_ray=128,
min_depth=0.1,
max_depth=volume_extent_world,
)
# 2) Instantiate the raymarcher.
# Here, we use the standard EmissionAbsorptionRaymarcher
# which marches along each ray in order to render
# the ray into a single 3D color vector
# and an opacity scalar.
raymarcher = EmissionAbsorptionRaymarcher()
# Finally, instantiate the implicit renders
# for both raysamplers.
renderer_grid = ImplicitRenderer(
raysampler=raysampler_grid, raymarcher=raymarcher,
)
renderer_mc = ImplicitRenderer(
raysampler=raysampler_mc, raymarcher=raymarcher,
)
在此单元格中，我们定义了NeuralRadianceField模块，该模块指定了场景的3D域上颜色和不透明度的连续场。
NeuralRadianceField（NeRF）的forward函数接收一组张量作为输入，这些张量参数化一束渲染光线。光线束随后转换为场景世界坐标中的3D光线点。然后，每个3D点使用HarmonicEmbedding层（在下一个单元格中定义）映射到谐波表示。然后，谐波嵌入进入NeRF模型的颜色和不透明度分支，以便分别用3D向量和[0-1]范围内的1D标量标记每个光线点，这些向量分别定义点的RGB颜色和不透明度。
由于NeRF具有较大的内存占用量，因此我们还实现了NeuralRadianceField.forward_batched方法。该方法将输入光线拆分为批次，并在for循环中分别对每个批次执行forward函数。这使我们能够渲染大量光线而不会耗尽GPU内存。通常，forward_batched将用于渲染从图像所有像素发出的光线，以生成场景的完整尺寸渲染。
class HarmonicEmbedding(torch.nn.Module):
def __init__(self, n_harmonic_functions=60, omega0=0.1):
"""
Given an input tensor `x` of shape [minibatch, ... , dim],
the harmonic embedding layer converts each feature
in `x` into a series of harmonic features `embedding`
as follows:
embedding[..., i*dim:(i+1)*dim] = [
sin(x[..., i]),
sin(2*x[..., i]),
sin(4*x[..., i]),
...
sin(2**(self.n_harmonic_functions-1) * x[..., i]),
cos(x[..., i]),
cos(2*x[..., i]),
cos(4*x[..., i]),
...
cos(2**(self.n_harmonic_functions-1) * x[..., i])
]
Note that `x` is also premultiplied by `omega0` before
evaluating the harmonic functions.
"""
super().__init__()
self.register_buffer(
'frequencies',
omega0 * (2.0 ** torch.arange(n_harmonic_functions)),
)
def forward(self, x):
"""
Args:
x: tensor of shape [..., dim]
Returns:
embedding: a harmonic embedding of `x`
of shape [..., n_harmonic_functions * dim * 2]
"""
embed = (x[..., None] * self.frequencies).view(*x.shape[:-1], -1)
return torch.cat((embed.sin(), embed.cos()), dim=-1)
class NeuralRadianceField(torch.nn.Module):
def __init__(self, n_harmonic_functions=60, n_hidden_neurons=256):
super().__init__()
"""
Args:
n_harmonic_functions: The number of harmonic functions
used to form the harmonic embedding of each point.
n_hidden_neurons: The number of hidden units in the
fully connected layers of the MLPs of the model.
"""
# The harmonic embedding layer converts input 3D coordinates
# to a representation that is more suitable for
# processing with a deep neural network.
self.harmonic_embedding = HarmonicEmbedding(n_harmonic_functions)
# The dimension of the harmonic embedding.
embedding_dim = n_harmonic_functions * 2 * 3
# self.mlp is a simple 2-layer multi-layer perceptron
# which converts the input per-point harmonic embeddings
# to a latent representation.
# Not that we use Softplus activations instead of ReLU.
self.mlp = torch.nn.Sequential(
torch.nn.Linear(embedding_dim, n_hidden_neurons),
torch.nn.Softplus(beta=10.0),
torch.nn.Linear(n_hidden_neurons, n_hidden_neurons),
torch.nn.Softplus(beta=10.0),
)
# Given features predicted by self.mlp, self.color_layer
# is responsible for predicting a 3-D per-point vector
# that represents the RGB color of the point.
self.color_layer = torch.nn.Sequential(
torch.nn.Linear(n_hidden_neurons + embedding_dim, n_hidden_neurons),
torch.nn.Softplus(beta=10.0),
torch.nn.Linear(n_hidden_neurons, 3),
torch.nn.Sigmoid(),
# To ensure that the colors correctly range between [0-1],
# the layer is terminated with a sigmoid layer.
)
# The density layer converts the features of self.mlp
# to a 1D density value representing the raw opacity
# of each point.
self.density_layer = torch.nn.Sequential(
torch.nn.Linear(n_hidden_neurons, 1),
torch.nn.Softplus(beta=10.0),
# Sofplus activation ensures that the raw opacity
# is a non-negative number.
)
# We set the bias of the density layer to -1.5
# in order to initialize the opacities of the
# ray points to values close to 0.
# This is a crucial detail for ensuring convergence
# of the model.
self.density_layer[0].bias.data[0] = -1.5
def _get_densities(self, features):
"""
This function takes `features` predicted by `self.mlp`
and converts them to `raw_densities` with `self.density_layer`.
`raw_densities` are later mapped to [0-1] range with
1 - inverse exponential of `raw_densities`.
"""
raw_densities = self.density_layer(features)
return 1 - (-raw_densities).exp()
def _get_colors(self, features, rays_directions):
"""
This function takes per-point `features` predicted by `self.mlp`
and evaluates the color model in order to attach to each
point a 3D vector of its RGB color.
In order to represent viewpoint dependent effects,
before evaluating `self.color_layer`, `NeuralRadianceField`
concatenates to the `features` a harmonic embedding
of `ray_directions`, which are per-point directions
of point rays expressed as 3D l2-normalized vectors
in world coordinates.
"""
spatial_size = features.shape[:-1]
# Normalize the ray_directions to unit l2 norm.
rays_directions_normed = torch.nn.functional.normalize(
rays_directions, dim=-1
)
# Obtain the harmonic embedding of the normalized ray directions.
rays_embedding = self.harmonic_embedding(
rays_directions_normed
)
# Expand the ray directions tensor so that its spatial size
# is equal to the size of features.
rays_embedding_expand = rays_embedding[..., None, :].expand(
*spatial_size, rays_embedding.shape[-1]
)
# Concatenate ray direction embeddings with
# features and evaluate the color model.
color_layer_input = torch.cat(
(features, rays_embedding_expand),
dim=-1
)
return self.color_layer(color_layer_input)
def forward(
self,
ray_bundle: RayBundle,
**kwargs,
):
"""
The forward function accepts the parametrizations of
3D points sampled along projection rays. The forward
pass is responsible for attaching a 3D vector
and a 1D scalar representing the point's
RGB color and opacity respectively.
Args:
ray_bundle: A RayBundle object containing the following variables:
origins: A tensor of shape `(minibatch, ..., 3)` denoting the
origins of the sampling rays in world coords.
directions: A tensor of shape `(minibatch, ..., 3)`
containing the direction vectors of sampling rays in world coords.
lengths: A tensor of shape `(minibatch, ..., num_points_per_ray)`
containing the lengths at which the rays are sampled.
Returns:
rays_densities: A tensor of shape `(minibatch, ..., num_points_per_ray, 1)`
denoting the opacity of each ray point.
rays_colors: A tensor of shape `(minibatch, ..., num_points_per_ray, 3)`
denoting the color of each ray point.
"""
# We first convert the ray parametrizations to world
# coordinates with `ray_bundle_to_ray_points`.
rays_points_world = ray_bundle_to_ray_points(ray_bundle)
# rays_points_world.shape = [minibatch x ... x 3]
# For each 3D world coordinate, we obtain its harmonic embedding.
embeds = self.harmonic_embedding(
rays_points_world
)
# embeds.shape = [minibatch x ... x self.n_harmonic_functions*6]
# self.mlp maps each harmonic embedding to a latent feature space.
features = self.mlp(embeds)
# features.shape = [minibatch x ... x n_hidden_neurons]
# Finally, given the per-point features,
# execute the density and color branches.
rays_densities = self._get_densities(features)
# rays_densities.shape = [minibatch x ... x 1]
rays_colors = self._get_colors(features, ray_bundle.directions)
# rays_colors.shape = [minibatch x ... x 3]
return rays_densities, rays_colors
def batched_forward(
self,
ray_bundle: RayBundle,
n_batches: int = 16,
**kwargs,
):
"""
This function is used to allow for memory efficient processing
of input rays. The input rays are first split to `n_batches`
chunks and passed through the `self.forward` function one at a time
in a for loop. Combined with disabling PyTorch gradient caching
(`torch.no_grad()`), this allows for rendering large batches
of rays that do not all fit into GPU memory in a single forward pass.
In our case, batched_forward is used to export a fully-sized render
of the radiance field for visualization purposes.
Args:
ray_bundle: A RayBundle object containing the following variables:
origins: A tensor of shape `(minibatch, ..., 3)` denoting the
origins of the sampling rays in world coords.
directions: A tensor of shape `(minibatch, ..., 3)`
containing the direction vectors of sampling rays in world coords.
lengths: A tensor of shape `(minibatch, ..., num_points_per_ray)`
containing the lengths at which the rays are sampled.
n_batches: Specifies the number of batches the input rays are split into.
The larger the number of batches, the smaller the memory footprint
and the lower the processing speed.
Returns:
rays_densities: A tensor of shape `(minibatch, ..., num_points_per_ray, 1)`
denoting the opacity of each ray point.
rays_colors: A tensor of shape `(minibatch, ..., num_points_per_ray, 3)`
denoting the color of each ray point.
"""
# Parse out shapes needed for tensor reshaping in this function.
n_pts_per_ray = ray_bundle.lengths.shape[-1]
spatial_size = [*ray_bundle.origins.shape[:-1], n_pts_per_ray]
# Split the rays to `n_batches` batches.
tot_samples = ray_bundle.origins.shape[:-1].numel()
batches = torch.chunk(torch.arange(tot_samples), n_batches)
# For each batch, execute the standard forward pass.
batch_outputs = [
self.forward(
RayBundle(
origins=ray_bundle.origins.view(-1, 3)[batch_idx],
directions=ray_bundle.directions.view(-1, 3)[batch_idx],
lengths=ray_bundle.lengths.view(-1, n_pts_per_ray)[batch_idx],
xys=None,
)
) for batch_idx in batches
]
# Concatenate the per-batch rays_densities and rays_colors
# and reshape according to the sizes of the inputs.
rays_densities, rays_colors = [
torch.cat(
[batch_output[output_i] for batch_output in batch_outputs], dim=0
).view(*spatial_size, -1) for output_i in (0, 1)
]
return rays_densities, rays_colors
在此函数中，我们定义了有助于神经辐射场优化的函数。
def huber(x, y, scaling=0.1):
"""
A helper function for evaluating the smooth L1 (huber) loss
between the rendered silhouettes and colors.
"""
diff_sq = (x - y) ** 2
loss = ((1 + diff_sq / (scaling**2)).clamp(1e-4).sqrt() - 1) * float(scaling)
return loss
def sample_images_at_mc_locs(target_images, sampled_rays_xy):
"""
Given a set of Monte Carlo pixel locations `sampled_rays_xy`,
this method samples the tensor `target_images` at the
respective 2D locations.
This function is used in order to extract the colors from
ground truth images that correspond to the colors
rendered using `MonteCarloRaysampler`.
"""
ba = target_images.shape[0]
dim = target_images.shape[-1]
spatial_size = sampled_rays_xy.shape[1:-1]
# In order to sample target_images, we utilize
# the grid_sample function which implements a
# bilinear image sampler.
# Note that we have to invert the sign of the
# sampled ray positions to convert the NDC xy locations
# of the MonteCarloRaysampler to the coordinate
# convention of grid_sample.
images_sampled = torch.nn.functional.grid_sample(
target_images.permute(0, 3, 1, 2),
-sampled_rays_xy.view(ba, -1, 1, 2), # note the sign inversion
align_corners=True
)
return images_sampled.permute(0, 2, 3, 1).view(
ba, *spatial_size, dim
)
def show_full_render(
neural_radiance_field, camera,
target_image, target_silhouette,
loss_history_color, loss_history_sil,
):
"""
This is a helper function for visualizing the
intermediate results of the learning.
Since the `NeuralRadianceField` suffers from
a large memory footprint, which does not let us
render the full image grid in a single forward pass,
we utilize the `NeuralRadianceField.batched_forward`
function in combination with disabling the gradient caching.
This chunks the set of emitted rays to batches and
evaluates the implicit function on one batch at a time
to prevent GPU memory overflow.
"""
# Prevent gradient caching.
with torch.no_grad():
# Render using the grid renderer and the
# batched_forward function of neural_radiance_field.
rendered_image_silhouette, _ = renderer_grid(
cameras=camera,
volumetric_function=neural_radiance_field.batched_forward
)
# Split the rendering result to a silhouette render
# and the image render.
rendered_image, rendered_silhouette = (
rendered_image_silhouette[0].split([3, 1], dim=-1)
)
# Generate plots.
fig, ax = plt.subplots(2, 3, figsize=(15, 10))
ax = ax.ravel()
clamp_and_detach = lambda x: x.clamp(0.0, 1.0).cpu().detach().numpy()
ax[0].plot(list(range(len(loss_history_color))), loss_history_color, linewidth=1)
ax[1].imshow(clamp_and_detach(rendered_image))
ax[2].imshow(clamp_and_detach(rendered_silhouette[..., 0]))
ax[3].plot(list(range(len(loss_history_sil))), loss_history_sil, linewidth=1)
ax[4].imshow(clamp_and_detach(target_image))
ax[5].imshow(clamp_and_detach(target_silhouette))
for ax_, title_ in zip(
ax,
(
"loss color", "rendered image", "rendered silhouette",
"loss silhouette", "target image", "target silhouette",
)
):
if not title_.startswith('loss'):
ax_.grid("off")
ax_.axis("off")
ax_.set_title(title_)
fig.canvas.draw(); fig.show()
display.clear_output(wait=True)
display.display(fig)
return fig
在这里，我们使用可微渲染进行辐射场拟合。
为了拟合辐射场，我们从target_cameras的视点对其进行渲染，并将生成的渲染与观察到的target_images和target_silhouettes进行比较。
比较是通过评估对应对target_images/rendered_images和target_silhouettes/rendered_silhouettes之间的平均huber（平滑l1）误差来完成的。
由于我们使用MonteCarloRaysampler，因此训练渲染器renderer_mc的输出是随机采样自图像平面的像素颜色，而不是形成有效图像的像素网格。因此，为了将渲染的颜色与地面实况进行比较，我们利用随机的蒙特卡罗像素位置在对应于渲染位置的xy位置对地面实况图像/轮廓target_silhouettes/rendered_silhouettes进行采样。这是使用辅助函数sample_images_at_mc_locs完成的，该函数在前面的单元格中进行了描述。
# First move all relevant variables to the correct device.
renderer_grid = renderer_grid.to(device)
renderer_mc = renderer_mc.to(device)
target_cameras = target_cameras.to(device)
target_images = target_images.to(device)
target_silhouettes = target_silhouettes.to(device)
# Set the seed for reproducibility
torch.manual_seed(1)
# Instantiate the radiance field model.
neural_radiance_field = NeuralRadianceField().to(device)
# Instantiate the Adam optimizer. We set its master learning rate to 1e-3.
lr = 1e-3
optimizer = torch.optim.Adam(neural_radiance_field.parameters(), lr=lr)
# We sample 6 random cameras in a minibatch. Each camera
# emits raysampler_mc.n_pts_per_image rays.
batch_size = 6
# 3000 iterations take ~20 min on a Tesla M40 and lead to
# reasonably sharp results. However, for the best possible
# results, we recommend setting n_iter=20000.
n_iter = 3000
# Init the loss history buffers.
loss_history_color, loss_history_sil = [], []
# The main optimization loop.
for iteration in range(n_iter):
# In case we reached the last 75% of iterations,
# decrease the learning rate of the optimizer 10-fold.
if iteration == round(n_iter * 0.75):
print('Decreasing LR 10-fold ...')
optimizer = torch.optim.Adam(
neural_radiance_field.parameters(), lr=lr * 0.1
)
# Zero the optimizer gradient.
optimizer.zero_grad()
# Sample random batch indices.
batch_idx = torch.randperm(len(target_cameras))[:batch_size]
# Sample the minibatch of cameras.
batch_cameras = FoVPerspectiveCameras(
R = target_cameras.R[batch_idx],
T = target_cameras.T[batch_idx],
znear = target_cameras.znear[batch_idx],
zfar = target_cameras.zfar[batch_idx],
aspect_ratio = target_cameras.aspect_ratio[batch_idx],
fov = target_cameras.fov[batch_idx],
device = device,
)
# Evaluate the nerf model.
rendered_images_silhouettes, sampled_rays = renderer_mc(
cameras=batch_cameras,
volumetric_function=neural_radiance_field
)
rendered_images, rendered_silhouettes = (
rendered_images_silhouettes.split([3, 1], dim=-1)
)
# Compute the silhouette error as the mean huber
# loss between the predicted masks and the
# sampled target silhouettes.
silhouettes_at_rays = sample_images_at_mc_locs(
target_silhouettes[batch_idx, ..., None],
sampled_rays.xys
)
sil_err = huber(
rendered_silhouettes,
silhouettes_at_rays,
).abs().mean()
# Compute the color error as the mean huber
# loss between the rendered colors and the
# sampled target images.
colors_at_rays = sample_images_at_mc_locs(
target_images[batch_idx],
sampled_rays.xys
)
color_err = huber(
rendered_images,
colors_at_rays,
).abs().mean()
# The optimization loss is a simple
# sum of the color and silhouette errors.
loss = color_err + sil_err
# Log the loss history.
loss_history_color.append(float(color_err))
loss_history_sil.append(float(sil_err))
# Every 10 iterations, print the current values of the losses.
if iteration % 10 == 0:
print(
f'Iteration {iteration:05d}:'
+ f' loss color = {float(color_err):1.2e}'
+ f' loss silhouette = {float(sil_err):1.2e}'
)
# Take the optimization step.
loss.backward()
optimizer.step()
# Visualize the full renders every 100 iterations.
if iteration % 100 == 0:
show_idx = torch.randperm(len(target_cameras))[:1]
show_full_render(
neural_radiance_field,
FoVPerspectiveCameras(
R = target_cameras.R[show_idx],
T = target_cameras.T[show_idx],
znear = target_cameras.znear[show_idx],
zfar = target_cameras.zfar[show_idx],
aspect_ratio = target_cameras.aspect_ratio[show_idx],
fov = target_cameras.fov[show_idx],
device = device,
),
target_images[show_idx][0],
target_silhouettes[show_idx][0],
loss_history_color,
loss_history_sil,
)
最后，我们通过从围绕体积的y轴旋转的多个视点进行渲染来可视化神经辐射场。
def generate_rotating_nerf(neural_radiance_field, n_frames = 50):
logRs = torch.zeros(n_frames, 3, device=device)
logRs[:, 1] = torch.linspace(-3.14, 3.14, n_frames, device=device)
Rs = so3_exp_map(logRs)
Ts = torch.zeros(n_frames, 3, device=device)
Ts[:, 2] = 2.7
frames = []
print('Rendering rotating NeRF ...')
for R, T in zip(tqdm(Rs), Ts):
camera = FoVPerspectiveCameras(
R=R[None],
T=T[None],
znear=target_cameras.znear[0],
zfar=target_cameras.zfar[0],
aspect_ratio=target_cameras.aspect_ratio[0],
fov=target_cameras.fov[0],
device=device,
)
# Note that we again render with `NDCMultinomialRaysampler`
# and the batched_forward function of neural_radiance_field.
frames.append(
renderer_grid(
cameras=camera,
volumetric_function=neural_radiance_field.batched_forward,
)[0][..., :3]
)
return torch.cat(frames)
with torch.no_grad():
rotating_nerf_frames = generate_rotating_nerf(neural_radiance_field, n_frames=3*5)
image_grid(rotating_nerf_frames.clamp(0., 1.).cpu().numpy(), rows=3, cols=5, rgb=True, fill=True)
plt.show()
在本教程中，我们展示了如何优化场景的隐式表示，以便从已知视点对场景进行渲染与每个视点的观察图像相匹配。渲染是使用PyTorch3D的隐式函数渲染器进行的，该渲染器由MonteCarloRaysampler或NDCMultinomialRaysampler和EmissionAbsorptionRaymarcher组成。