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Title: Experimental investigation of multiclass 3D point cloud completion
Authors: Lee, Benedict Wei Zheng
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lee, B. W. Z. (2022). Experimental investigation of multiclass 3D point cloud completion. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: Point clouds captured in real world environments face challenges which cause them to be incomplete or ambiguous to varying degrees. While numerous solutions have been proposed to tackle these challenges, there have been fewer attempts at taking a multiclass approach for completing point clouds. A multiclass approach may generalize better to ambiguity of the partial point clouds by completing them according to a range of specified or plausible class outcomes. In this project, experiments were conducted using various methodologies for integrating multiclass shape completion capabilities into an existing shape completion neural network with and without the use of a conditional Generative Adversarial Network (GAN). The resulting multiclass pretrained models and their completed point clouds are also evaluated, with the feasibility of their implementation and performance of their results being discussed.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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