Strivec: Sparse Tri-Vector Radiance Fields
This work addresses the computational and memory efficiency challenges in neural radiance field modeling for 3D scene reconstruction, representing an incremental improvement over existing tensor-based methods.
The authors tackled the problem of efficiently representing 3D scenes as radiance fields by proposing Strivec, a method that uses sparsely distributed local tensor grids with CP decomposition, achieving better rendering quality with significantly fewer parameters than prior methods like TensoRF and Instant-NGP.
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.