GAMES Webinar 2019 – 84期（CVPR 2018三维视觉论文报告）| 杨耀青（卡耐基梅隆大学），张寅达（普林斯顿大学）

【GAMES Webinar 2019-84期（CVPR 2018三维视觉论文报告）】
报告嘉宾1：杨耀青，卡耐基梅隆大学
报告时间：2019年1月31日 晚8:00-8:45（北京时间）
主持人：徐凯，国防科技大学（个人主页：http://www.kevinkaixu.net）
报告题目：折纸网点云自动编码器
报告摘要：
三维点云网络上的深度学习是近年来计算机图形学的一个重要方向。在报告中，我将向大家介绍一种新型的三维点云无监督学习网络——折纸网自动编码器。它的核心思想是把点云的生成看作一个折纸的过程，从而使用低复杂度的网络生成点云。同时，因为这个折纸的自动编码过程由一个全局码字来表示，它可以得到好的嵌入，进而提供优良的无监督和半监督学习效果。希望能够从这次报告中，听取大家的意见，给之后的研究提供帮助。
讲者简介：
杨耀青目前是卡耐基梅隆大学的在读博士生。他的研究兴趣是三维视觉中的深度学习处理，和大规模分布式计算中的差错控制编码计算方法。在读博士之前，杨耀青在清华大学获得本科学位。
讲者个人主页：https://sites.google.com/site/yangyaoqingcmu/

报告嘉宾2：张寅达，普林斯顿大学
报告时间：2019年1月31日 晚8:45-9:30（北京时间）
主持人：徐凯，国防科技大学（个人主页：http://www.kevinkaixu.net）
报告题目：From Pixels to Scene: Recovering 3D Geometry and Semantics for Indoor Environments
报告摘要：
Understanding 3D geometry and semantics of the surrounding environment is in critically high demand for many applications, such as autonomous driving, robotics, augmented reality, etc. However, it is extremely challenging due to the low quality depth measurements due to failures and noisy measurements from sensors, limited access to ground truth data, and cluttered scenes with heavy occlusions and intervening objects. In this presentation, I will introduces a full spectrum of 3D scene understanding works to handle these challenging issues. Starting from estimating a depth map, which is one of the most important immediate measurements of the 3D geometry of the scene, we introduce a learning based active stereo system that learns self-supervisely and reduces the disparity error to 1/10th of other canonical stereo systems. To further handle the missing depth caused by sensor failures, we propose a method to effectively complete the depth map using information from an aligned color image. Beyond per pixel depth, we then attempt to predict other high-level semantics on each pixel, such as surface normals and object boundaries. However, realizing the lack of large scale supervision, we design a synthetic data generation framework, which creates photo-realistic color rendering and various of accurate pixel-wise ground truths to facilitate the learning process and improve the performance on real data. In the end, we pursue holistic scene understanding by estimating a 3D representation of the scene, in which objects and room layout are represented using 3D bounding box and planar surface respectively. We propose methods to produce such representation from either a single color panorama or depth image leveraging scene context. On the whole, these proposed methods produce understanding of both 3D geometry and semantics from the most fine-grained pixel level to the holistic scene scale, which build foundations and could possibly inspire future works for 3D scene understanding.
讲者简介：
Yinda Zhang received his Ph.D. in Computer Science from Princeton University, advised by Professor Thomas Funkhouser. Before that, he received a Bachelor degree from Dept. Automation in Tsinghua University, and a Master degree from Dept. ECE in National University of Singapore co-supervised by Prof. Ping Tan and Prof. Shuicheng Yan. His research mainly focus on machine learning, computer vision, and computer graphics. Recently, he was working on 3D scene understanding, where the goal is to measure 3D geometry and semantics of the surrounding environment leveraging deep learning technology.
讲者个人主页：http://www.zhangyinda.com

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