SlimmeRF：可調整的輻射場

摘要

最近，神經輻射場（Neural Radiance Field，NeRF）及其變體已成為成功的方法，用於新視角合成和3D場景重建。然而，大多數目前的NeRF模型要麼通過使用大型模型大小來實現高準確性，要麼通過權衡準確性來實現高內存效率。這限制了任何單個模型的應用範圍，因為高準確性模型可能不適合於低內存設備，而內存高效模型可能無法滿足高質量要求。為此，我們提出了SlimmeRF，一個允許在模型大小和準確性之間進行即時測試時間權衡的模型，通過減輕使模型同時適用於不同計算預算的情況。我們通過一種新提出的名為Tensorial Rank Incrementation（TRaIn）的算法實現了這一點，該算法在訓練期間逐漸增加模型的張量表示的秩。我們還觀察到，我們的模型在稀疏視圖情況下允許更有效的權衡，有時甚至在瘦身後實現更高的準確性。我們歸功於這樣一個事實，即錯誤信息（如浮體）往往存儲在對應於較高秩的組件中。我們的實現可在https://github.com/Shiran-Yuan/SlimmeRF找到。

English

Neural Radiance Field (NeRF) and its variants have recently emerged as successful methods for novel view synthesis and 3D scene reconstruction. However, most current NeRF models either achieve high accuracy using large model sizes, or achieve high memory-efficiency by trading off accuracy. This limits the applicable scope of any single model, since high-accuracy models might not fit in low-memory devices, and memory-efficient models might not satisfy high-quality requirements. To this end, we present SlimmeRF, a model that allows for instant test-time trade-offs between model size and accuracy through slimming, thus making the model simultaneously suitable for scenarios with different computing budgets. We achieve this through a newly proposed algorithm named Tensorial Rank Incrementation (TRaIn) which increases the rank of the model's tensorial representation gradually during training. We also observe that our model allows for more effective trade-offs in sparse-view scenarios, at times even achieving higher accuracy after being slimmed. We credit this to the fact that erroneous information such as floaters tend to be stored in components corresponding to higher ranks. Our implementation is available at https://github.com/Shiran-Yuan/SlimmeRF.

SlimmeRF：可調整的輻射場

SlimmeRF: Slimmable Radiance Fields

摘要

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