Strivec: Sparse Tri-Vector Radiance Fields
We propose Strivec, a novel neural representation that models a 3D scene as a
radiance field with sparsely distributed and compactly factorized local tensor
feature grids. Our approach leverages tensor decomposition, following the
recent work TensoRF, to model the tensor grids. In contrast to TensoRF which
uses a global tensor and focuses on their vector-matrix decomposition, we
propose to utilize a cloud of local tensors and apply the classic
CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into triple
vectors that express local feature distributions along spatial axes and
compactly encode a local neural field. We also apply multi-scale tensor grids
to discover the geometry and appearance commonalities and exploit spatial
coherence with the tri-vector factorization at multiple local scales. The final
radiance field properties are regressed by aggregating neural features from
multiple local tensors across all scales. Our tri-vector tensors are sparsely
distributed around the actual scene surface, discovered by a fast coarse
reconstruction, leveraging the sparsity of a 3D scene. We demonstrate that our
model can achieve better rendering quality while using significantly fewer
parameters than previous methods, including TensoRF and Instant-NGP.
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Quankai Gao
claimed_verified
2023-07-26T14:30:57.019Z
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Quankai Gao
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Quankai
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Qiangeng Xu
claimed_verified
2023-07-27T08:49:12.082Z
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qiangeng xu
user
xharlie
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Hao Su
extracted_pending
2023-09-24T22:40:34.944Z
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Hao Su
user
rawer
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Ulrich Neumann
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Zexiang Xu
claimed_verified
2023-11-17T07:41:46.938Z
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/avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
Zexiang Xu
user
zexiangxu
Strivec: Sparse Tri-Vector Radiance Fields
We propose Strivec, a novel neural representation that models a 3D scene as a
radiance field with sparsely distributed and compactly factorized local tensor
feature grids. Our approach leverages tensor decomposition, following the
recent work TensoRF, to model the tensor grids. In contrast to TensoRF which
uses a global tensor and focuses on their vector-matrix decomposition, we
propose to utilize a cloud of local tensors and apply the classic
CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into triple
vectors that express local feature distributions along spatial axes and
compactly encode a local neural field. We also apply multi-scale tensor grids
to discover the geometry and appearance commonalities and exploit spatial
coherence with the tri-vector factorization at multiple local scales. The final
radiance field properties are regressed by aggregating neural features from
multiple local tensors across all scales. Our tri-vector tensors are sparsely
distributed around the actual scene surface, discovered by a fast coarse
reconstruction, leveraging the sparsity of a 3D scene. We demonstrate that our
model can achieve better rendering quality while using significantly fewer
parameters than previous methods, including TensoRF and Instant-NGP.
Strivec models a 3D scene as a radiance field using sparsely distributed and compactly factorized local tensor grids via CP decomposition, outperforming methods like TensoRF and Instant-NGP with fewer parameters.
radiance field sparsely distributed compactly factorized local tensor feature grids tensor decomposition TensoRF CANDECOMP/PARAFAC (CP) decomposition local feature distributions multi-scale tensor grids spatial coherence tri-vector factorization neural field aggregating neural features