title: Strivec: Sparse Tri-Vector Radiance Fields
summary: 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.
paper_authors._id: 64c09901e062a72f15b477d1
paper_authors.name: Quankai Gao
paper_authors.status: claimed_verified
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paper_authors.fullname: Quankai Gao
paper_authors.type: user
paper_authors.user: Quankai
paper_authors._id: 64c09901e062a72f15b477d2
paper_authors.name: Qiangeng Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-27T08:49:12.082Z
paper_authors._id: 64c1a214c84cd12dd79c1530
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paper_authors.fullname: qiangeng xu
paper_authors.type: user
paper_authors.user: xharlie
paper_authors._id: 64c09901e062a72f15b477d3
paper_authors.name: Hao Su
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-09-24T22:40:34.944Z
paper_authors._id: 638055ad54b1953f5340fe66
paper_authors.avatarUrl: /avatars/9ad62192954c3ecf7d73573c33ccd857.svg
paper_authors.fullname: Hao Su
paper_authors.type: user
paper_authors.user: rawer
paper_authors._id: 64c09901e062a72f15b477d4
paper_authors.name: Ulrich Neumann
paper_authors._id: 64c09901e062a72f15b477d5
paper_authors.name: Zexiang Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-17T07:41:46.938Z
paper_authors._id: 65565b1b8616d8d740abb976
paper_authors.avatarUrl: /avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
paper_authors.fullname: Zexiang Xu
paper_authors.type: user
paper_authors.user: zexiangxu
paper_title: Strivec: Sparse Tri-Vector Radiance Fields
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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