Due to hardware limitations, 3D sensors like LiDAR often produce sparse and
noisy point clouds. Point cloud upsampling is the task of converting such point
clouds into dense and clean ones. This thesis tackles the problem of point cloud upsampling
using deep neural networks. The effectiveness of a point cloud upsampling
neural network heavily relies on the upsampling module and the feature extractor used
therein. In this thesis, I propose a novel point upsampling module, called NodeShuffle.
NodeShuffle leverages Graph Convolutional Networks (GCNs) to better encode
local point information from point neighborhoods. NodeShuffle is versatile and can
be incorporated into any point cloud upsampling pipeline. Extensive experiments
show how NodeShuffle consistently improves the performance of previous upsampling
methods. I also propose a new GCN-based multi-scale feature extractor, called Inception
DenseGCN. By aggregating features at multiple scales, Inception DenseGCN
learns a hierarchical feature representation and enables further performance gains. I
combine Inception DenseGCN with NodeShuffle into the proposed point cloud upsampling
network called PU-GCN. PU-GCN sets new state-of-art performance with
much fewer parameters and more efficient inference.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/665984 |
| Date | 16 November 2020 |
| Creators | Qian, Guocheng |
| Contributors | Ghanem, Bernard, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Wonka, Peter, Pottmann, Helmut |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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