title: SlimmeRF: Slimmable Radiance Fields
summary: 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.
paper_authors._id: 657fd5ffa2ef3216752cfb22
paper_authors.name: Shiran Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-18T08:47:45.412Z
paper_authors._id: 657fe4da869d5bb0e5bd3d17
paper_authors.avatarUrl: /avatars/7ff9761adcc7b9b7292946e505221669.svg
paper_authors.fullname: Shiran Yuan
paper_authors.type: user
paper_authors.user: Shiran-Yuan
paper_authors._id: 657fd5ffa2ef3216752cfb23
paper_authors.name: Hao Zhao
paper_title: SlimmeRF: Slimmable Radiance Fields
paper_summary: 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.
paper_ai_summary: SlimmeRF, using Tensorial Rank Incrementation, allows dynamic trading of NeRF model size and accuracy, suitable for varying computational resources and sparse-view scenarios.
paper_ai_keywords: Neural Radiance Field NeRF model size accuracy memory-efficiency instant test-time trade-offs SlimmeRF Tensorial Rank Incrementation TRaIn sparse-view scenarios floaters