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.

翻译：神经辐射场（NeRF）及其变体近年来已成为新颖视角合成与三维场景重建的成功方法。然而，当前大多数NeRF模型要么通过使用大模型尺寸实现高精度，要么通过牺牲精度来提升内存效率。这种特性限制了单一模型的适用场景：高精度模型可能无法适配低内存设备，而内存高效模型又难以满足高质量需求。为此，我们提出SlimmeRF——一种可通过瘦身技术在测试阶段即时权衡模型尺寸与精度的模型，从而使其能同时适用于不同计算预算的场景。我们通过新提出的张量秩递增算法实现这一目标，该算法在训练过程中逐步提升模型张量表示的秩。我们还发现，该模型在稀疏视角场景中能实现更有效的权衡，有时甚至能在瘦身后获得更高精度。我们将其归因于浮游物等错误信息倾向于存储在对应更高秩的分量中。本实现代码已开源至https://github.com/Shiran-Yuan/SlimmeRF。