【英語学習　NeRF】NeRF: Neural Radiance Fields（ニューラル・ラディエンス・フィールド）

we present neural radiance fields or
nerf a new method for representing
complex scenes that achieves
state-of-the-art results review
synthesis given a set of input images of
a scene which in this visualization is a
Lego bulldozer we optimize a volumetric
representation of the scene as a
vector-valued function which is defined
for any continuous 5d coordinate
consisting of a location and view
direction we parameterize this scene
representation as a fully connected deep
network that takes each single 5d
coordinate and outputs the corresponding
volume density and view dependent
emitted RGB radiance at that location we
can then use techniques from volume
rendering to composite these values
along a camera array to render any pixel
this rendering is fully differentiable
so we are able to optimize the scene
representation by minimizing the error
of rendering all camera rates from a
collection of standard RGB images we
first show comparisons to top performing
methods on synthetically rendered data
scene representation networks implicitly
represent a scene using a fully
connected neural network as some runs
have issues of multi-view consistency
and are unable to represent high
frequency details local light field
fusion uses a pre trained network to
promote each input view to a high
resolution 3d volume inconsistencies
between adjacent volumes causes
flickering artifacts neural volumes in
coda seen as 128 cubed voxel grid and
use a work field to better allocate
these limited samples however they’re
still unable to represent high
resolution content such as the fine
details in the ship’s texture and
rigging we see the same trend in all of
our synthetic test scenes our method
both qualitatively and quantitatively
outperforms the other methods
here we show the results of our method
on the six other objects in a
realistically rendered dataset with
complex geometry and non Lambert
materials next we will show comparisons
on real scenes captured with a
forward-facing phone camera SSRN uses a
recurrent network to rain march through
encoded scenes resulting in inconsistent
appearance between rendered views lff
blends between multiple renderings with
different limited fields of view
resulting in flickering artifacts along
image borders here only our method can
represent fine details with complex
occlusion effects such as the ribs in
this fossilized t-rex skeleton now let’s
take a look at some additional real
results we can visualize the view
dependent effects captured by our
representation such as the
semi-transparent appearance the olive
oil bottle and speckle areas on the
stove reflections on this room’s
television and specularity x’ on the
conference table and sharp reflections
and specularity x’ on this red car’s
body and windshield our representation
captures high quality scene geometry
such as the details on this red flower
and complex occlusions in the Christmas
tree leaves and ornaments
this geometry is precise enough to be
used for additional graphics
applications such as virtual object
insertion with convincing occlusion
effects
here we visualize additional results
across a wide variety of real-world
scenes each captured with only 20 to 50
input images our neural radiance field
representations are able to render photo
realistic novel views of entire scenes
with fine geometry details and realistic
view dependent reflectance effects we’re
able to capture all of this visual
complexity in just the weights of a
simple fully connected Network
we also show preliminary results for 360
degree capture of real scenes

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