FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple
  Super-Resolution Pipeline
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.
658260475def15befa2eee73
Chien-Yu Lin
admin_assigned
2023-12-20T09:53:12.834Z
64d1950a0fbfb00b919e9481
/avatars/a4a487431b9ae4a8ad916a8b553ec5b2.svg
Chien-Yu Lin
user
cylinbao
658260475def15befa2eee74
Qichen Fu
admin_assigned
2023-12-20T09:57:11.580Z
6496114d33611fb433f16970
/avatars/ac722222c39e87c772705a3bf52118ad.svg
Qichen Fu
user
janemiminini
658260475def15befa2eee75
Thomas Merth
admin_assigned
2023-12-20T09:57:20.471Z
64e67344a12333aea5fbf549
/avatars/83b95306bf8232219a3fb9ee278740d7.svg
Thomas Merth
user
tmerthapple
658260475def15befa2eee76
Karren Yang
658260475def15befa2eee77
Anurag Ranjan
admin_assigned
2023-12-20T09:57:58.868Z
6317f42c33a4e2b5d3b38a93
/avatars/18f9c81b2f34de888138adaa47817fcc.svg
Anurag Ranjan
user
anuragr
FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple
  Super-Resolution Pipeline
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.
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.
NeRF SR super-resolution neural radiance fields random patch sampling