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    Neural Adaptive SCEne Tracing

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    2202.13664.pdf
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    40.50Mb
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    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Li, Rui cc
    Rückert, Darius
    Wang, Yuanhao cc
    Idoughi, Ramzi
    Heidrich, Wolfgang cc
    KAUST Department
    Computational Imaging Group
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Electrical and Computer Engineering Program
    Visual Computing Center (VCC)
    Date
    2022-03-16
    Permanent link to this record
    http://hdl.handle.net/10754/677958
    
    Metadata
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    Abstract
    Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost. While the most recent generation of such methods has made progress on the rendering (inference) times, very little progress has been made on improving the reconstruction (training) times. In this work, we present Neural Adaptive Scene Tracing (NAScenT), the first neural rendering method based on directly training a hybrid explicit-implicit neural representation. NAScenT uses a hierarchical octree representation with one neural network per leaf node and combines this representation with a two-stage sampling process that concentrates ray samples where they matter most near object surfaces. As a result, NAScenT is capable of reconstructing challenging scenes including both large, sparsely populated volumes like UAV captured outdoor environments, as well as small scenes with high geometric complexity. NAScenT outperforms existing neural rendering approaches in terms of both quality and training time.
    Publisher
    arXiv
    arXiv
    2202.13664
    Additional Links
    https://arxiv.org/pdf/2202.13664.pdf
    Collections
    Preprints; Computer Science Program; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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