孙龙华，女，讲师。2017年毕业于山东科技大学，获学士学位；2024年毕业于北京工业大学，获博士学位，主要从事基于深度学习的二维/三维数据处理方法研究。现担任信息科学与工程学院院长助理。在科研方面，在《IEEE Transactions on Circuits and Systems for Video Technology》《The Visual Computer》等国内外知名期刊和国际会议上发表学术论文12篇，其中SCI期刊论文2篇，累计影响因子11.90，在ICME（CCF-B类会议）、ICIP（CCF-C类会议）等国际会议上发表论文5篇；在EI检索国际会议上发表论文4篇。在教研方面，参与多项教学改革项目，包括：参与省部级教学改革课题2项，其中重点课题1项、面上课题1项、参与山东省专项治理课题1项、主持教育部协同育人产教融合项目1项。

主要研究方向为：深度学习、二维图像重建、三维点云数据处理、三维点云数据压缩。

主讲课程：《机器学习》、《人工智能专业导论》、《大学生信息素养》等。

主要论文成果：

[1] J Wang, L Sun*, R Xiong, Y Shi, Q Zhu, B Yin. Depth Map Super-resolution Based on Dual Normal-depth Regularization and Graph Laplacian Prior[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(6): 3304-3318. (SCI 1 区TOP期刊, IF: 8.4, 见刊)

[2] L Sun*, J Wang, J Liu, J Yu and Q Zhu. Cluster-Based Two-Branch Framework for Point Cloud Attribute Compression[J]. The Visual Computer. 2023:1-14. (SCI 3 区, IF: 3.5, 检索)

[3] L Sun*, J Wang, R Xiong, Y Shi, Q Zhu, B Yin. Dual Regularization Based Depth Map Superresolution with Graph Laplacian Prior[C]. IEEE International Conference on Multimedia and Expo(ICME), 2021. (CCF B类国际会议, Oral, 检索)

[4] L Sun*, J Wang, Y Shi, Q Zhu, B Yin, N Lin. Octree-Based Temporal-Spatial Context Entropy Model for LiDAR Point Cloud Compression[C]. IEEE International Conference on Visual Communication and Image Processing(VCIP). 2023. (EI国际会议, 检索)

[5] L. Sun*, J. Wang, and Q. Zhu. Surface Normal Data Guided Depth Map Restoration with Edge-Preserving Smoothing Regularization[C]. 12th International Conference on Digital Image Processing, (ICDIP). 2020. (EI国际会议, 检索).

[6] L. Sun*, J. Wang, Y. Shi, Q. Zhu and B. Yin. Surface Normal Data Guided Depth Recovery with Graph Laplacian Regularization[C]. ACM International Conference on Multimedia in Asia (ACM MM ASIA). 2019.(CCF C类国际会议, 检索).

[7] J. Yu, J. Wang，L. Sun*, M Wu and Q. Zhu. Point Cloud Geometry Compression Based on Multi-layer Residual Structure[J]. Entropy. 2022, 24(11): 1677. (SCI 3 区, IF:2.7, 检索).

[8] J. Yu, L. Sun*, J. Wang and Q. Zhu. A Multi-layer Residual Architecture for Point Cloud Geometry Compression[C]. International Conference on Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI), 2022. (EI国际会议, 最佳论文奖, 检索).

[9] Y Wang, S. Wang, and L. Sun*. Point Cloud Up-sampling via a Coarse-to-Fine Network[C]. International Conference on Multimedia Modeling(MMM), 2022. (CCF C类国际会议, 检索).

[10] Y. Wang, J. Wang, Y. Shi, L. Sun* and B. Yin. LGP-Net: Local Geometry Preserving Network for Point Cloud Completion[C]. IEEE International Conference on Multimedia and Expo (ICME), 2022. (CCF B类国际会议, 检索).

[11] J. Liu, L. Sun*, J. Wang, J. Pei and Q. Zhu. Cluster-Based Point Cloud Attribute Compression using Inter Prediction and Graph Fourier Transform[C]. The 14th International Conference on Digital Image Processing(ICDIP), 2022. (EI 国际会议, 检索).

[12] W. Xu, J. Wang, L. Sun* and Q. Zhu. Depth Map Super-Resolution by Multi-Direction Dictionary and Joint Regularization[C]. IEEE International Conference on Image Processing (ICIP). 2022. (CCF C类 国际会议, 检索).