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Sequence Generation Completion Method and Resolution Scaling Network for Point Cloud Completion
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DOI：10.1109/TGRS.2024.3409612.png)
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Abstract
En 中文
Point cloud completion aims to predict the missing part for an incomplete 3-D shape. Existing point cloud completion methods based on deep learning complete the point cloud by extracting global features from the incomplete point cloud. However, such methods cannot generate a uniformly distributed point cloud and the accurate structure details of the object. To solve the problem, a novel method for completing point clouds is proposed in this article. Our approach is a two-step strategy. First, to predict the sparse point cloud with uniform density, the sequence generation completion (SGC) method is proposed. By numbering the subspace obtained from the spatial subdivision, the point cloud is represented with a sequence of numbers and the point cloud completion problem is turned into a sequence generation problem. Second, to obtain the dense point cloud and generate the accurate structural details of point clouds, we propose a resolution scaling network (RSN). This network takes local resolution as input and increases the weight of low-resolution regions by learning to preserve the comprehensive structural information of the sparse point cloud, which is crucial to generate dense point cloud. The comprehensive experiments on several public datasets demonstrate the effectiveness of our method. Source code and pretrained models will be available at github.com/Pikachu-NCU/Sequence-Generate-Completion-Method.
Keywords:
Point cloud compression
Task analysis
Shape
Feature extraction
Predictive models
Neural networks
Training
Neural network
point cloud completion
resolution scale network
sequence generation completion (SGC)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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