Application of Neural Rendering Based Visual Synthesis in Construction Scene

doi:10.3969/j.issn.1006-2475.2023.12.013

Abstract

Abstract: Abstract: When neural radiation field （NeRF） is applied to outdoor construction scenes， due to the difficulty in capturing the lighting in outdoor scenes and the large difference between the foreground and background ranges of construction scenes， blurring and artifacts will appear in novel views. Through analysis， an improved visual synthesis method is proposed. First， camera parameters are obtained from RGB images through SFM algorithm to represent outdoor construction scenes. Then， the vector generated from the pre training encoder is introduced and added to the rendering network to reduce the impact of light. Finally， the foreground and background in the image are separated for volume rendering， which improves the effect of visual synthesis. Based on the outdoor construction scene data set， compared with three methods， the results show that the proposed method improves the peak signal-to-noise ratio and structural similarity by 12.2% and 10.9% respectively compared with the best method. It appears that the novel views generated by the proposed method in outdoor construction scenes have better finesse.

Key words: Key words: NeRF, visual synthesis, construction scenario, illumination, range

CLC Number:

TP391

ZHANG Zai-cheng, LI Jian. Application of Neural Rendering Based Visual Synthesis in Construction Scene[J]. Computer and Modernization, 2023, 0(12): 76-81.

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