Strivec：稀疏三向量輻射場

摘要

我們提出了Strivec，一種新穎的神經表示，將3D場景建模為一個輻射場，其中包含稀疏分佈和緊湊分解的本地張量特徵網格。我們的方法利用張量分解，遵循最近的TensoRF工作，來建模這些張量網格。與TensoRF不同，TensoRF使用全局張量並專注於它們的向量-矩陣分解，我們建議利用一組本地張量並應用經典的CANDECOMP/PARAFAC（CP）分解，將每個張量分解為三元向量，這些向量表達了沿空間軸的本地特徵分佈並緊湊編碼了本地神經場。我們還應用多尺度張量網格來發現幾何和外觀的共同特點，並利用在多個本地尺度上的三元向量分解來利用空間一致性。最終的輻射場特性是通過從所有尺度的多個本地張量中聚合神經特徵來回歸的。我們的三元向量張量稀疏分佈在實際場景表面周圍，通過快速粗略重建來發現，利用3D場景的稀疏性。我們展示了我們的模型可以在使用比以前的方法（包括TensoRF和Instant-NGP）更少的參數的情況下實現更好的渲染質量。

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：稀疏三向量輻射場

Strivec: Sparse Tri-Vector Radiance Fields

摘要

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