Merge fVDB into OpenVDB #1808
Closed
Merge fVDB into OpenVDB #1808
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Signed-off-by: Ken Museth <[email protected]>
Signed-off-by: Ken Museth <[email protected]>
fwilliams
requested review from
kmuseth,
apradhana,
jmlait,
Idclip,
danrbailey and
richhones
as code owners
Signed-off-by: Francis Williams <[email protected]>
kmuseth
reviewed
May 28, 2024
apradhana
reviewed
Jun 11, 2024
| "editor.rulers": [ | ||
| 120 | ||
| ], | ||
| <<<<<<< Updated upstream |
apradhana
reviewed
Jun 11, 2024
| "--generated-members=numpy.* ,torch.*" | ||
| ], | ||
| "python.analysis.typeCheckingMode": "basic", | ||
| ======= |
There was a problem hiding this comment.
This also should be fixed.
Signed-off-by: Francis Williams <[email protected]>
Signed-off-by: Francis Williams <[email protected]>
Signed-off-by: Francis Williams <[email protected]>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
𝑓VDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence
fVDB is a novel GPU-optimized framework for deep learning on large-scale 3D data. It provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, attention, ray-tracing, meshing, etc.
fVDB simultaneously provides a much larger feature set (primitives and operators) than established frameworks with no loss in efficiency: our operators match or exceed the performance of other frameworks with narrower scope. Furthermore, fVDB can process datasets with much larger footprint and spatial resolution than prior works, while providing a competitive memory footprint on small inputs. To achieve this combination of versatility and performance, fVDB relies on the novel NanoVDB index grid acceleration structure paired with several key innovations including GPU accelerated sparse grid construction, convolution using tensor cores, fast ray tracing kernels using a Hierarchical Digital Differential Analyzer algorithm (HDDA), and jagged tensors.
Our framework is fully integrated with PyTorch enabling interoperability with existing pipelines, and we demonstrate its effectiveness on a number of representative tasks such as large-scale point-cloud segmentation, high resolution 3D generative modeling, unbounded scale Neural Radiance Fields, and large-scale point cloud reconstruction.
Crucially, fVDB has no external dependencies outside the OpenVDB repository other than PyTorch, and it is fully optional (in the same way that NanoVDB and Houdini extensions are).
There is an associated SIGGRAPH paper describing the technical details of fVDB which will be released soon.