三维场景表示的体渲染增强与冗余约束分析
摘要
引入体渲染计算,使高斯贡献沿光线连续累积以减轻无序带来的误差;并结合透射率裁剪剔除冗余椭球,同时加入
多视角一致性约束提高几何稳定性。实验结果显示,该方法可在多类数据集上有效减少噪声、提升颜色与深度图的
可靠性,具有一定工程应用价值。
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[1]Kerbl B, Kopanas G, Leimkühler T, et al. 3d
gaussian splatting for real-time radiance field rendering[J].
ACM Trans. Graph., 2023, 42(4): 139:1-139:14.
[2]Bakurov I, Buzzelli M, Schettini R, et al. Structural
similarity index (SSIM) revisited: A data-driven approach[J].
Expert Systems with Applications, 2022, 189: 116087.
[3]Hedman P, Philip J, Price T, et al. Deep blending for
free-viewpoint image-based rendering[J]. ACM Transactions
on Graphics (ToG), 2018, 37(6): 1-15.
[4]Wang P, Liu L, Liu Y, et al. Neus: Learning neural
implicit surfaces by volume rendering for multi-view
reconstruction[C]//Proceedings of the 35th International
Conference on Neural Information Processing Systems. Red
Hook: ACM, 2021: 2081-2094.
[5]Yariv L, Gu J, Kasten Y, et al. Volume rendering of
neural implicit surfaces[J]. Advances in Neural Information
Processing Systems, 2021, 34: 4805-4815.
[6]Huang B, Yu Z, Chen A, et al. 2d gaussian splatting for
geometrically accurate radiance fields[C]//ACM SIGGRAPH
2024 conference papers. Colorado: ACM, 2024: 1-11.
[7]Yu Z, Sattler T, Geiger A. Gaussian opacity fields:
Efficient adaptive surface reconstruction in unbounded
scenes[J]. ACM Transactions on Graphics (TOG), 2024,
43(6): 1-13.
[8]Barron J T, Mildenhall B, Verbin D, et al. Mipnerf 360: Unbounded anti-aliased neural radiance fields[C]//
Proceedings of the IEEE/CVF conference on computer vision
and pattern recognition. New Orleans: IEEE Press, 2022:
5470-5479.
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