基于改进张量辐射场的农作物三维重建
摘要
机技术的桥梁,通过三维重建与模拟技术,为农业研究与实践开辟了新途径。它能够多角度捕捉植物图像并进行精
准重建与仿真,帮助深入理解植物生长过程,提升作物产量与质量。此外,虚拟植物还能模拟不同农业管理方法和
环境因素的影响,为农业决策提供支持。传统三维重建技术依赖昂贵设备,如激光扫描和双目立体视觉,成本高且
耗时长,限制了普及。而基于神经辐射场(NeRF)的方法仅需单目摄像头,利用普通设备即可实现重建,前景广
阔。但NeRF模型计算复杂且耗时长。为此,本研究采用改进的张量辐射场(TensoRF)模型,结合深度估计神经网
络和球谐函数编码,提升三维结构学习和图像生成质量,增强复杂空间处理和细节捕捉能力。
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[1]Mildenhall B , Srinivasan P P , Tancik M ,etal.NeRF: Representing Scenes as Neural Radiance Fields for
View Synthesis[J]. 2020.DOI:10.48550/arXiv.2003.08934.
[2]Chen A, Xu Z, Geiger A, et al. Tensorf: Tensorial
radiance fields[C]//European conference on computer vision.
Cham: Springer Nature Switzerland, 2022: 333-350.
[3]Yang L, Kang B, Huang Z, et al. Depth anything:
Unleashing the power of large-scale unlabeled data[C]//
Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition. 2024: 10371-10381.
[4]Atkinson K, Han W. Spherical harmonics and
approximations on the unit sphere: an introduction[M].
Springer Science & Business Media, 2012.
[5]Korhonen J, You J. Peak signal-to-noise ratio revisited:
Is simple beautiful?[C]//2012 Fourth international workshop on
quality of multimedia experience. IEEE, 2012: 37-38.
[6]Wang Z, Bovik A C, Sheikh H R, et al. Image quality
assessment: from error visibility to structural similarity[J]. IEEE
transactions on image processing, 2004, 13(4): 600-612.
[7]Zhang R, Isola P, Efros A A, et al. The unreasonable
effectiveness of deep features as a perceptual metric[C]//
Proceedings of the IEEE conference on computer vision and
pattern recognition. 2018: 586-595.
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