Papers › TensoRF: Tensorial Radiance Fields
TensoRF: Tensorial Radiance Fields
Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, Hao Su
We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which represents a 3D voxel grid with per-voxel multi-channel features. Our central idea is to factorize the 4D scene tensor into multiple compact low-rank tensor components. We demonstrate that applying traditional CP decomposition -- that factorizes tensors into rank-one components with compact vectors -- in our framework leads to improvements over vanilla NeRF. To further boost performance, we introduce a novel vector-matrix (VM) decomposition that relaxes the low-rank constraints for two modes of a tensor and factorizes tensors into compact vector and matrix factors. Beyond superior rendering quality, our models with CP and VM decompositions lead to a significantly lower memory footprint in comparison to previous and concurrent works that directly optimize per-voxel features. Experimentally, we demonstrate that TensoRF with CP decomposition achieves fast reconstruction (<30 min) with better rendering quality and even a smaller model size (<4 MB) compared to NeRF. Moreover, TensoRF with VM decomposition further boosts rendering quality and outperforms previous state-of-the-art methods, while reducing the reconstruction time (<10 min) and retaining a compact model size (<75 MB).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Low-Dose X-Ray Ct Reconstruction | X3D | TensoRF | PSNR | 33.78 | #3 of 9 | Archive leaderboard | report |
| Low-Dose X-Ray Ct Reconstruction | X3D | TensoRF | SSIM | 0.9387 | #3 of 9 | Archive leaderboard | report |
| Novel View Synthesis | X3D | TensoRF | PSNR | 37.67 | #3 of 6 | Archive leaderboard | report |
| Novel View Synthesis | X3D | TensoRF | SSIM | 0.9712 | #3 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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