Speakers
Haruo Fujiwara
(Hakuhodo DY Holdings Inc.)
Ryutaro Nagai
(blueqat inc.)
Hiroshi Kato
(Hakuhodo DY Holdings Inc.)
Description
Neural Radiance Field (NeRF) is a well-known 3D reconstruction method capable of generating novel views of a target scene. NeRF model often employs a neural network trained by captured images to represent a 3D scene as a continuous function that maps a 3D coordinate and a view direction to color and density. In this work, we examine the potential of NeRF acceleration by replacing the MLP layers of a standard NeRF architecture with Matrix Product Operators (MPO). We show that our preliminary experiments with NeRF-MPO, our NeRF variant, can efficiently reduce model size with comparable performance, indicating the prospect of applying tensor networks to NeRF.
Primary authors
Haruo Fujiwara
(Hakuhodo DY Holdings Inc.)
Ryutaro Nagai
(blueqat inc.)
Hiroshi Kato
(Hakuhodo DY Holdings Inc.)
Yuichiro Minato
(blueqat inc.)
