Few TensoRF: Enhance the Few-shot on Tensorial Radiance Fields
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Abstract (arXiv)
This paper presents Few TensoRF, a 3D reconstruction framework that combines TensorRF's efficient tensor based representation with FreeNeRF's frequency driven few shot regularization. Using TensorRF to significantly accelerate rendering speed and introducing frequency and occlusion masks, the method improves stability and reconstruction quality under sparse input views. Experiments on the Synthesis NeRF benchmark show that Few TensoRF method improves the average PSNR from 21.45 dB (TensorRF) to 23.70 dB, with the fine tuned version reaching 24.52 dB, while maintaining TensorRF's fast (≈10-15) minute training time. Experiments on the THuman 2.0 dataset further demonstrate competitive performance in human body reconstruction, achieving 27.37 - 34.00 dB with only eight input images. These results highlight Few TensoRF as an efficient and data effective solution for real-time 3D reconstruction across diverse scenes.
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References · 12
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
- 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
- 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
- 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
- 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,194
- 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 750
- 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
- 2020Space-time Neural Irradiance Fields for Free-Viewpoint Videocited by 414
- 2023FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularizationcited by 310
- 2021ICON: Implicit Clothed humans Obtained from Normalscited by 283
- 2021Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensorscited by 246
- 2022ECON: Explicit Clothed humans Optimized via Normal integrationcited by 199
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