View-Guided Point Cloud Completion
This addresses the problem of incomplete 3D point clouds for computer vision applications, representing an incremental improvement by integrating image data.
The paper tackles point cloud completion by introducing a view-guided method that uses a single-view image to provide missing global structure, achieving significantly superior results over existing solutions on a new large-scale dataset.
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.