Abstract

This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extra single-view image. By leveraging a framework that sequentially performs effective cross-modality and cross-level fusions, our method achieves significantly superior results over typical existing solutions on a new large-scale dataset we collect for the view-guided point cloud completion task.

Keywords:
Computer science Point cloud Task (project management) Cloud computing Point (geometry) Modality (human–computer interaction) Image (mathematics) Artificial intelligence Scale (ratio) Data mining Computer vision Machine learning Mathematics

Metrics

79
Cited By
10.78
FWCI (Field Weighted Citation Impact)
54
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

3D Shape Modeling and Analysis
Physical Sciences →  Engineering →  Computational Mechanics
3D Surveying and Cultural Heritage
Physical Sciences →  Earth and Planetary Sciences →  Geology
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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