[1] XU Y, CHEN R, HUANG H Jet al等. Analog study of ground-penetrating radar technology for lava tube detection[J]. Journal of Deep Space Exploration, 11, 365-373(2024).

     徐懿, 陈锐, 黄汉钧. 探地雷达对天体熔岩管探测技术类比研究[J]. 深空探测学报（中英文）, 11, 365-373(2024).

[2] HORZ F. Lava tubes-potential shelters for habitats[C], 405-412(1985).

[3] WANG T L, AO L, HAN N et al. Time-of-flight camera intensity image reconstruction based on an untrained convolutional neural network[J]. Photonics, 11, 821(2024).

[4] WANG T L, AO L, ZHENG J et al. Reconstructing depth images for time-of-flight cameras based on second-order correlation functions[J]. Photonics, 10, 1223(2023).

[5] 彭嫚, 刘召芹, 万文辉. 基于多源影像的月面地形融合方法研究[C](2024).

     PENG M, LIU ZH Q, WAN W H et al. Research on lunar topography fusion method based on multi-source images[C](2024).

[6] HUANG Z J, WEN Y X, WANG Z H et al. Surface reconstruction from point clouds： a survey and a benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 9727-9748(2024).

[7] ZHAO CH, ZHANG B M, YU D H et al. Airborne LiDAR point cloud classification using transfer learning[J]. Opt. Precision Eng., 27, 1601-1612(2019).

     赵传, 张保明, 余东行. 利用迁移学习的机载激光雷达点云分类[J]. 光学 精密工程, 27, 1601-1612(2019).

[8] LIPMAN Y, COHEN-OR D, LEVIN D et al. Parameterization-free projection for geometry reconstruction[J]. ACM Transactions on Graphics, 26, 22(2007).

[9] LI Z, CHEN Z, QU F et al. Point resampling and ray transformation aid to editable NeRF models[J]. arXiv preprint arXiv：, 2024.

[10] ALEXA M, BEHR J, COHEN-OR D et al. Computing and rendering point set surfaces[J]. IEEE Transactions on Visualization and Computer Graphics, 9, 3-15(2003).

[11] DING Y Q, CHEN Z, JI Y et al. Light field-based underwater 3D reconstruction via angular re-sampling[J]. IEEE Transactions on Computational Imaging, 9, 881-893(2023).

[12] HUANG H, WU S H, GONG M L et al. Edge-aware point set resampling[J]. ACM Transactions on Graphics, 32, 1-12(2013).

[13] PETRACEK P, ALEXIS K, SASKA M. RMS： redundancy-minimizing point cloud sampling for real-time pose estimation[J]. ArXiv e-Prints(2023).

[14] 李安琪, 郑艳, 王博. 基于3D点云深度学习上采样的建筑物精细化三维重建[J]. 现代信息科技, 7, 136-139， 144(2023).

     LI A Q, ZHENG Y, WANG B et al. Refined 3D reconstruction of buildings based on 3D point cloud deep learning upsampling[J]. Modern Information Technology, 7, 136-139， 144(2023).

[15] ZHANG T Y. Research on High-quality Point Cloud Resampling Based on Centroidal Voronoi Tessellation[D](2019).

     张铁. 基于重心Voronoi剖分的高质量点云重采样研究[D](2019).

[16] WEN C, YU B S, TAO D C. Learnable skeleton-aware 3D point cloud sampling[C], 17, 17671-17681(2023).

[17] AI G, FANG L, FENG ZH Y. Improved network of PU-GAN for point cloud upsampling[J]. Computer Engineering and Design, 45, 2461-2467(2024).

     艾国, 方立, 冯站银. 改进PU-GAN的点云上采样网络[J]. 计算机工程与设计, 45, 2461-2467(2024).

[18] 张湘, 韩少腾. 点云数据重采样下的破损建筑虚拟重建与修复方法[J]. 科技通报, 39, 23-26(2023).

     ZHANG X, HAN SH T. Virtual reconstruction and repair of damaged buildings based on point cloud data resampling[J]. Bulletin of Science and Technology, 39, 23-26(2023).

[19] DUNBAR D, HUMPHREYS G. A spatial data structure for fast Poisson-disk sample generation[J]. ACM Transactions on Graphics, 25, 503-508(2006).

[20] QUAN W Z, GUO J W, ZHANG Y K et al. Maximal Poisson-disk sampling by sampling radius optimization[J]. Scientia Sinica （Informationis）, 47, 442-454(2017).

     全卫泽, 郭建伟, 张义宽. 基于采样半径优化的最大化Poisson圆盘采样[J]. 中国科学： 信息科学, 47, 442-454(2017).

[21] HAUSDORFF F. Grundzüge der Mengenlehre[M]. von Veit(1914).

[22] ULAND W, WARREN W, HEATHER J et al. NASA Planetary Pits and Caves Analog Dataset[EB/OL]. December(2014).

[23] XU Y, LIU X, CAO X et al. Artificial intelligence： A powerful paradigm for scientific research[J]. The Innovation, 2(2021).