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dc.contributor.authorWu, Yunjieen_US
dc.contributor.authorSun, Zhengxingen_US
dc.contributor.editorJacobson, Alec and Huang, Qixingen_US
dc.description.abstractLearning-based 3D generation is a popular research field in computer graphics. Recently, some works adapted implicit function defined by a neural network to represent 3D objects and have become the current state-of-the-art. However, training the network requires precise ground truth 3D data and heavy pre-processing, which is unrealistic. To tackle this problem, we propose the DFR, a differentiable process for rendering implicit function representation of 3D objects into 2D images. Briefly, our method is to simulate the physical imaging process by casting multiple rays through the image plane to the function space, aggregating all information along with each ray, and performing a differentiable shading according to every ray's state. Some strategies are also proposed to optimize the rendering pipeline, making it efficient both in time and memory to support training a network. With DFR, we can perform many 3D modeling tasks with only 2D supervision. We conduct several experiments for various applications. The quantitative and qualitative evaluations both demonstrate the effectiveness of our method.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.subjectDifferentiable Rendering
dc.subjectbased 3D generation
dc.subjectImplicit Function Representation for 3D objects CCS Concepts
dc.subjectComputing methodologies
dc.subjectShape inference
dc.subjectRay tracing
dc.subjectVolumetric models
dc.titleDFR: Differentiable Function Rendering for Learning 3D Generation from Imagesen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersMachine Learning and Analysis

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  • 39-Issue 5
    Geometry Processing 2020 - Symposium Proceedings

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License