Strivec：稀疏三向量辐射场

摘要

我们提出了Strivec，这是一种新颖的神经表示，将3D场景建模为具有稀疏分布和紧凑分解的本地张量特征网格的辐射场。我们的方法利用张量分解，遵循最近的工作TensoRF，来建模张量网格。与TensoRF不同，后者使用全局张量并侧重于它们的向量-矩阵分解，我们提出利用一组本地张量并应用经典的CANDECOMP/PARAFAC（CP）分解，将每个张量分解为表达沿空间轴的本地特征分布并紧凑编码本地神经场的三元向量。我们还应用多尺度张量网格来发现几何和外观的共同特性，并利用三元向量分解在多个本地尺度上的空间一致性。最终的辐射场属性通过聚合来自所有尺度的多个本地张量的神经特征进行回归。我们的三元向量张量稀疏分布在实际场景表面周围，通过快速粗略重建发现，利用了3D场景的稀疏性。我们证明，我们的模型可以在使用比以前的方法（包括TensoRF和Instant-NGP）显著更少的参数的情况下实现更好的渲染质量。

English

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：稀疏三向量辐射场

Strivec: Sparse Tri-Vector Radiance Fields

摘要

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