An Evolution of Learning Neural Implicit Representations for 3D Shapes
Neural implicit representations are the immediate precursors to neural radiance fields (NeRF). In a short span of only four years, they have quickly become the representation of choice for learning reconstructive and generative models of 3D shapes. Unlike traditional convolutional neural networks that have been widely applied to reason about images and video, neural implicit models encode shape boundaries in a continuous manner to lead to superior visual quality; they are also amenable to simple network architectures to facilitate a variety of extensions and adaptations. In this talk, I will recount a brief history of the development of neural implicit representations, while focusing mainly on several paths of follow-ups from our recent works, including structured implicit models, direct mesh generation, CSG assemblies, and the use of contextual, query-specific feature encoding for category-agnostic and generalizable shape representation learning.
ZHANG Hao, Richard is a professor in the School of Computing Science at Simon Fraser University, Canada. Currently, he holds a Distinguished University Professorship and is an Amazon Scholar. Richard earned his Ph.D. from the University of Toronto, and MMath and BMath degrees from the University of Waterloo. His research is in computer graphics and visual computing with special interests in geometric modeling, shape analysis, 3D vision, geometric deep learning, as well as computational design and fabrication. He has published more than 180 papers on these topics, including over 60 articles in SIGGRAPH (+Asia) and ACM Transactions on Graphics (TOG), the top venue in computer graphics. Awards won by Richard include a Canadian Human-Computer Communications Society Achievement Award in Computer Graphics (2022), a Google Faculty Award (2019), a National Science Foundation of China Overseas Outstanding Young Researcher Award (2015), an NSERC Discovery Accelerator Supplement Award (2014), a Best Dataset Award from ChinaGraph (2020), as well as faculty grants/gifts from Adobe, Autodesk, Google, and Huawei. He and his students have won the CVPR 2020 Best Student Paper Award and Best Paper Awards at SGP 2008 and CAD/Graphics 2017.
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