Position-aware Guided Point Cloud Completion with CLIP Model
Journal:
arXiv
Published Date:
Dec 11, 2024
Abstract
Point cloud completion aims to recover partial geometric and topological
shapes caused by equipment defects or limited viewpoints. Current methods
either solely rely on the 3D coordinates of the point cloud to complete it or
incorporate additional images with well-calibrated intrinsic parameters to
guide the geometric estimation of the missing parts. Although these methods
have achieved excellent performance by directly predicting the location of
complete points, the extracted features lack fine-grained information regarding
the location of the missing area. To address this issue, we propose a rapid and
efficient method to expand an unimodal framework into a multimodal framework.
This approach incorporates a position-aware module designed to enhance the
spatial information of the missing parts through a weighted map learning
mechanism. In addition, we establish a Point-Text-Image triplet corpus PCI-TI
and MVP-TI based on the existing unimodal point cloud completion dataset and
use the pre-trained vision-language model CLIP to provide richer detail
information for 3D shapes, thereby enhancing performance. Extensive
quantitative and qualitative experiments demonstrate that our method
outperforms state-of-the-art point cloud completion methods.
