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.

提出了一种名为 Strivec 的新型神经表示方法，将 3D 场景建模为稀疏分布且紧凑分解的局部张量特征网格辐射场，并使用 CANDECOMP/PARAFAC（CP）分解和多尺度张量网格来实现对几何和外观的发现与利用，优于之前的方法并使用更少的参数。