Summary of Position-aware Guided Point Cloud Completion with Clip Model, by Feng Zhou et al.
Position-aware Guided Point Cloud Completion with CLIP Model
by Feng Zhou, Qi Zhang, Ju Dai, Lei Li, Qing Fan, Junliang Xing
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach in this paper aims to improve point cloud completion by incorporating multimodal features. Current methods rely on 3D coordinates or additional images with well-calibrated intrinsic parameters, but lack fine-grained information about the missing area. The authors propose a rapid and efficient method that expands an unimodal framework into a multimodal one, using a position-aware module to enhance spatial information through weighted map learning. Additionally, they establish a Point-Text-Image triplet corpus PCI-TI and MVP-TI based on existing datasets, and leverage pre-trained vision-language model CLIP for richer detail information. The method outperforms state-of-the-art point cloud completion methods in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making incomplete 3D shapes complete again. Right now, there are two main ways to do this: one uses only the 3D coordinates of the shape, and the other uses extra images with well-calibrated cameras. However, these methods don’t give us enough information about what’s missing. To fix this, the authors created a new way to combine different types of data (3D shapes, text, and images) to get more details about the missing parts. They also made a big dataset of 3D shapes with corresponding text and image descriptions. By using this combined approach, they were able to make better predictions than previous methods. |
Keywords
» Artificial intelligence » Language model
