NVIDIA Introduces Deep-Learning Framework fVDB for Enhanced Spatial Intelligence
NVIDIA
has
unveiled
its
new
deep-learning
framework,
fVDB,
designed
to
build
spatial
intelligence
from
real-world
3D
data.
According
to
the
NVIDIA
Technical
Blog,
fVDB
aims
to
solve
the
inefficiencies
and
performance
bottlenecks
that
come
with
piecing
together
various
libraries
for
spatial
intelligence.
Challenges
in
Spatial
Intelligence
Generative
physical
AI
models
require
spatial
intelligence
to
understand
and
navigate
the
3D
space
of
the
physical
world.
Traditionally,
developers
have
had
to
use
a
patchwork
of
different
libraries
to
build
frameworks
for
spatial
intelligence,
leading
to
bugs,
inefficiencies,
and
performance
bottlenecks.
Introducing
fVDB
NVIDIA’s
fVDB
framework
is
designed
to
handle
sparse,
large-scale,
and
high-performance
spatial
intelligence.
Leveraging
OpenVDB,
an
industry-standard
for
the
efficient
storage
and
simulation
of
sparse
volumetric
data,
fVDB
integrates
deep
learning
operators
with
NanoVDB,
NVIDIA’s
GPU-accelerated
implementation
of
OpenVDB.
fVDB
is
an
open-source
extension
to
PyTorch,
enabling
a
complete
set
of
deep-learning
operations
on
large
3D
data.
Key
capabilities
include
compatibility
with
existing
VDB
datasets,
a
unified
API
for
neural
network
training,
ray
tracing,
and
rendering,
and
faster,
more
scalable
performance.
Applications
of
fVDB
fVDB
is
already
being
utilized
by
NVIDIA
Research,
NVIDIA
DRIVE,
and
NVIDIA
Omniverse
teams.
Notable
applications
include:
-
Surface
Reconstruction:
Neural
Kernel
Surface
Reconstruction
(NKSR)
leverages
fVDB
to
reconstruct
high-fidelity
surfaces
from
large
point
clouds. -
Generative
AI:
XCube
combines
diffusion
generative
models
with
sparse
voxel
hierarchies,
enabling
the
generation
of
3D
scenes
with
high
spatial
resolution. -
NeRFs:
NeRF-XL
uses
fVDB
to
distribute
neural
radiance
fields
across
multiple
GPUs
for
large-scale
3D
rendering.
Future
Developments
NVIDIA
plans
to
integrate
fVDB
functionality
into
NVIDIA
NIM
microservices,
enabling
developers
to
incorporate
fVDB
into
Universal
Scene
Description
(OpenUSD)
workflows
within
NVIDIA
Omniverse.
Upcoming
NVIDIA
NIM
microservices
include
fVDB
Mesh
Generation,
fVDB
Physics
Super-Res,
and
fVDB
NeRF-XL,
which
will
generate
OpenUSD-based
geometry
using
Omniverse
Cloud
APIs.
Conclusion
Developed
by
NVIDIA,
fVDB
is
a
groundbreaking
deep-learning
framework
for
sparse,
large-scale
spatial
intelligence.
It
builds
on
OpenVDB
to
enable
applications
such
as
digital
twins,
neural
radiance
fields,
and
3D
generative
AI.
For
more
details,
visit
the
official
NVIDIA
announcement.
Image
source:
Shutterstock
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