Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence(2022)
摘要
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a powerful 3D generative model for 3D shape synthesis and analysis. This paper proposes a deep 3D energy-based model to represent volumetric shapes. The maximum likelihood training of the...
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关键词
Three-dimensional displays,Solid modeling,Shape,Data models,Training,Analytical models,Feature extraction
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