Senior Principal Research Scientist
Dr. Dong Tian joined InterDigital recently as a Senior Principal Research Scientist after working with Computer Vision Group of Mitsubishi Electric Research Laboratories (MERL) at Cambridge, MA, U.S. between 2010-2018. Previously, he conducted researches with Thomson Corporate Research at Princeton, NJ from 2006 and Tampere University of Technology in Finland from 2002. He has been actively contributing to video related standards in MPEG within projects including H.264/MPEG AVC (2002-), MVC (2006-), H.265/HEVC extensions such as MV-HEVC, 3DV (2010-), and point cloud compression (2016-). His current research interests cover point cloud processing, graph signal processing, deep learning, and computer vision. Besides numerous publications on top-tier conferences and transactions, he holds over 30 US-granted patents. In addition, he serves as technical committee member in multiple IEEE societies. Dong received M.Eng. and B.Eng. degrees on automation from the University of Science and Technology of China (USTC) in 1998 and 1995, respectively; and he was granted Ph.D. at Beijing University of Technology in 2002. He is a senior member of IEEE.
演讲:Point Cloud Compression, Processing and Understanding
2018-10-20 15:00 - 15:45
Point cloud data has been emerging and popular for various applications including virtual reality (VR), augmented reality (AR), plus many other computer vision problems. Comparing to conventional image/video data, several fundamental challenges need to be addressed, e.g., its large data rate, irregular sampling structure, varying geometric as well as associated attributes. In this talk, we first review recent progress in point cloud compression and an ongoing MPEG standardization attempt. A framework to conduct fast point cloud resampling is then presented based graph signal processing, which could be utilized to scale down the complexity of many point cloud processing tasks. Last, deep neural networks (DNNs) are generalized for native supporting on point clouds as a new type of signal. For one example, a folding-based autoencoder (AE) architecture FoldingNet is introduced with great potentials for point cloud classification tasks. In short, fundamental techniques are highlighted how point clouds could be successful with more efficient compression, processing and understanding.
Faouzi Kossentini
The world is today witnessing a revolutionary transformation in the areas of media delivery, processing and consumption. Not only the internet traffic is already dominated by the exchange of visual information but also the visual cloud has become of the center of such traffic. Towards a fast-growing visual cloud, with underlying mostly-Xeon-populated homogeneous data centers, Intel Corporation is now leading the way with an Open Source SW-centric strategy. Intel is already open sourcing its SVT-HEVC encoder in Q3’2018, and it also plans to open source the SVT-AV1 encoder in Q2’2019. Such will increase adoption and decrease the cost of HEVC and AV1 by our visual cloud customers, helping them to accelerate the growth of their visual cloud applications. We will discuss briefly Intel’s current visual cloud SW strategy as well as provide detailed descriptions of the being-open-sourced SVT-HEVC encoder and the soon-to-be-open-sourced SVT-AV1 encoder. We will also present results that will illustrate the performance-quality tradeoffs of each of the SVT-HEVC and SVT-AV1 encoders. Finally, we will invite our visual cloud customers to participate in the development and growth of the new SVT-HEVC and SVT-AV1 Open Source communities.
Debargha Mukherjee
The Alliance for Open Media - a consortium of major Internet companies formed in 2016 with the mission to develop open media formats for the web - closed their first video codec AV1 in June of 2018. AV1 is the best standardized video codec available today that is also royalty-free. This talk will provide a high level overview of the coding tools in AV1, with special attention to the tools and features that are industry-first in standardized codecs. Results on standard tests sets will be provided.