李竹：多尺度稀疏卷积学习在点云压缩、超分辨率和去块效应中的应用
- 题目: 多尺度稀疏卷积学习在点云压缩、超分辨率和去块效应中的应用
- 主讲人：李竹 @ 美国密苏里大学堪萨斯分校教授
- 时间：2025年3月14日 10:30 - 12:00
- 地点：工学院南楼326会议室
主讲人简介
Zhu Li is now a professor with the Dept of Computer Science & Electrical Engineering (CSEE), University of Missouri,Kansas City, and the director of the NSF Center for Big Learning at UMKC. He was Sr. Staff Researcher/Sr. Manager with with Samsung Research America's Multimedia Standards Research Lab in Richardson, TX, 2012-2015, Sr. Staff Researcher/Media Analytics Group Lead with FutureWei (Huawei) Technology's Media Lab in Bridgewater, NJ, 2010~2012, and an Assistant Professor with the Dept of Computing, The Hong Kong Polytechnic University from 2008 to 2010, and a Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, from 2000 to 2008.
讲座简介
Due to the increased popularity of augmented and virtual reality experiences, as well as 3D sensing for auto-driving, the interest in capturing high resolution real-world point clouds has grown significantly in recent years. Point cloud is a new class of signal that is non-uniform and sparse and this present unique challenges to the signal processing, compression and learning problems. In this talk, we present our multi-scale sparse convolutional learning and Graph Fourier Transform (GFT) based framework for large scale point cloud processing, with applications to the geometry and attributes super-resolution, and dynamic point cloud compression with latent space compensation. The architecture is memory efficient and can learn deep networks to handle large scale point cloud in real world applications. Initial results demonstrated that this framework achieved new state of the art results in geometry super-resolution, attributes deblocking and super-resolving, and dynamic point cloud sequence compression.
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