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Progressive NeRF Training With Dynamic Frequency Allocation for Sparse-View Synthesis
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AI
DOI：10.1109/LSP.2026.3675910.png)
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Abstract
En 中文
NeRF's performance degrades under sparse view conditions due to overfitting to limited input data. Recent works have alleviated this issue by linearly expanding the visible frequency bands of NeRF's inputs as training progresses, which means they allocate equal time to low frequency and high frequency components. However, we observe that learning low frequency components requires less training time, whereas learning high frequency components demands more. Based on this insight, we propose a Dynamic Position Encoding (DPE) mask that allocates additional optimization time to higher frequency bands as they are gradually activated. To further improve the efficiency of NeRF training, we introduce a Multi-Stage Training (MST) paradigm: we generate four RGB image scales via three 2 & times; downsamplings of training images and NeRF is optimized from the lowest resolution to the original resolution in the final stage for high rendering quality. Extensive experiments on the LLFF and DTU benchmarks demonstrate that our approach not only synthesizes high-fidelity renderings with only three input views but also achieves a remarkable 3.1 & times; reduction in training time.
Keywords:
NeRF
dynamic frequency regularization
multi-stage training
NeRF
dynamic frequency regularization
multi-stage training
Journal
I
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3.9
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600
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0