title: FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple
    Super-Resolution Pipeline
summary: Super-resolution (SR) techniques have recently been proposed to upscale the
  outputs of neural radiance fields (NeRF) and generate high-quality images with
  enhanced inference speeds. However, existing NeRF+SR methods increase training
  overhead by using extra input features, loss functions, and/or expensive
  training procedures such as knowledge distillation. In this paper, we aim to
  leverage SR for efficiency gains without costly training or architectural
  changes. Specifically, we build a simple NeRF+SR pipeline that directly
  combines existing modules, and we propose a lightweight augmentation technique,
  random patch sampling, for training. Compared to existing NeRF+SR methods, our
  pipeline mitigates the SR computing overhead and can be trained up to 23x
  faster, making it feasible to run on consumer devices such as the Apple
  MacBook. Experiments show our pipeline can upscale NeRF outputs by 2-4x while
  maintaining high quality, increasing inference speeds by up to 18x on an NVIDIA
  V100 GPU and 12.8x on an M1 Pro chip. We conclude that SR can be a simple but
  effective technique for improving the efficiency of NeRF models for consumer
  devices.
paper_authors._id: 658260475def15befa2eee73
paper_authors.name: Chien-Yu Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:53:12.834Z
paper_authors._id: 64d1950a0fbfb00b919e9481
paper_authors.avatarUrl: /avatars/7a5f367882012bd08a3ca73f9acd9912.svg
paper_authors.fullname: Chien-Yu Lin
paper_authors.type: user
paper_authors.user: cylinbao
paper_authors._id: 658260475def15befa2eee74
paper_authors.name: Qichen Fu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:57:11.580Z
paper_authors._id: 6496114d33611fb433f16970
paper_authors.avatarUrl: /avatars/ac722222c39e87c772705a3bf52118ad.svg
paper_authors.fullname: Qichen Fu
paper_authors.type: user
paper_authors.user: janemiminini
paper_authors._id: 658260475def15befa2eee75
paper_authors.name: Thomas Merth
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:57:20.471Z
paper_authors._id: 64e67344a12333aea5fbf549
paper_authors.avatarUrl: /avatars/83b95306bf8232219a3fb9ee278740d7.svg
paper_authors.fullname: Thomas Merth
paper_authors.type: user
paper_authors.user: tmerthapple
paper_authors._id: 658260475def15befa2eee76
paper_authors.name: Karren Yang
paper_authors._id: 658260475def15befa2eee77
paper_authors.name: Anurag Ranjan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:57:58.868Z
paper_authors._id: 6317f42c33a4e2b5d3b38a93
paper_authors.avatarUrl: /avatars/18f9c81b2f34de888138adaa47817fcc.svg
paper_authors.fullname: Anurag Ranjan
paper_authors.type: user
paper_authors.user: anuragr
paper_title: FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple
    Super-Resolution Pipeline
paper_summary: Super-resolution (SR) techniques have recently been proposed to upscale the
  outputs of neural radiance fields (NeRF) and generate high-quality images with
  enhanced inference speeds. However, existing NeRF+SR methods increase training
  overhead by using extra input features, loss functions, and/or expensive
  training procedures such as knowledge distillation. In this paper, we aim to
  leverage SR for efficiency gains without costly training or architectural
  changes. Specifically, we build a simple NeRF+SR pipeline that directly
  combines existing modules, and we propose a lightweight augmentation technique,
  random patch sampling, for training. Compared to existing NeRF+SR methods, our
  pipeline mitigates the SR computing overhead and can be trained up to 23x
  faster, making it feasible to run on consumer devices such as the Apple
  MacBook. Experiments show our pipeline can upscale NeRF outputs by 2-4x while
  maintaining high quality, increasing inference speeds by up to 18x on an NVIDIA
  V100 GPU and 12.8x on an M1 Pro chip. We conclude that SR can be a simple but
  effective technique for improving the efficiency of NeRF models for consumer
  devices.
paper_ai_summary: A lightweight NeRF+SR pipeline uses random patch sampling to enhance efficiency without costly training or major architectural changes, enabling faster inference and high-quality upscaling on consumer devices.
paper_ai_keywords: NeRF SR super-resolution neural radiance fields random patch sampling