Neural Radiance Field by Joint Sampling

doi:10.13190/j.jbupt.2021-095

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (6): 109-115.doi: 10.13190/j.jbupt.2021-095

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Neural Radiance Field by Joint Sampling

LIU Shao-hua¹, LI Ming-hao^1,2, LI Zhao-xin², MAO Tian-lu², LIU Jing³

1. Cyber-Physical Systems Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
3. School of Software, Hebei Normal University, Shijiazhuang 050024, China

Received:2021-05-23 Online:2021-12-28 Published:2021-12-28

Abstract

Abstract: Compared with the traditional light field, the neural reflectance field (NeRF) method uses the neural network to fit the light sampling of scenes, which implicitly encodes the light field from input images to render novel view. However, NeRF method requires long training time and has slow rendering speed. To solve this problem, a joint sampling-based NeRF is proposed, which can make the coarse network and fine network share uniform sampling results, thereby accelerating the network training and view synthesis by reducing unnecessary light sampling. The experiments demonstrate that, in the case of the similar view synthesis quality, compared with the baseline method, the proposed method can reduce the training time by 20% and improve the view synthesis efficiency by 25%.

Key words: neural reflectance field, light sampling, joint sampling, view synthesis

CLC Number:

TN911.22

LIU Shao-hua, LI Ming-hao, LI Zhao-xin, MAO Tian-lu, LIU Jing. Neural Radiance Field by Joint Sampling[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(6): 109-115.

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