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Creators/Authors contains: "Sitar, Edward M"

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  1. ; (, IEEE)
  2. ; (, ACM)
  3. ; ; (, ACM MobiSys)
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  4. ; ; (, Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing)
    null (Ed.)
    3D Point Cloud (PCD) is an efficient machine representation for surrounding environments and has been used in many applications. But a fast reconstruction of complete PCD for large environments remains a challenge. We propose AutoPCD, a machine-learning model that reconstructs complete PCDs, under sensor occlusion and poor lighting conditions. AutoPCD splits the PCD into multiple parts, approximates them by several 3D planes, and independently learns the plane features for reconstruction. We have experimentally evaluated AutoPCD in a large indoor hallway environment. 
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    Accepted Manuscript Full Text Available