---
title: "IE-PMMA:Point Cloud Completion Through Inverse Edge-aware Upsampling and Precise Multi-Modal Feature Alignment"
authors:
  - "Ran Jia"
  - "Junpeng Xue"
  - "Shuai Ma"
  - "Wenbo Lu"
  - "Kelei Wang"
doi: "10.24963/ijcai.2025/138"
url: "https://ijcai.org/proceedings/2025/138"
pdf: "https://ijcai.org/proceedings/2025/0138.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "1233-1241"
track: "Main Track"
keywords:
  - "Computer Vision: CV: Multimodal learning"
  - "Computer Vision: CV: 3D computer vision"
---

# IE-PMMA:Point Cloud Completion Through Inverse Edge-aware Upsampling and Precise Multi-Modal Feature Alignment

Ran Jia, Junpeng Xue, Shuai Ma, Wenbo Lu, Kelei Wang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 1233-1241. DOI: [10.24963/ijcai.2025/138](https://doi.org/10.24963/ijcai.2025/138)

## Abstract

Point cloud completion is a crucial task in 3D computer vision. Multi-modal completion approaches have gained attention among the popular two-stage point cloud completion methods. However, there is a notable lack of research focused on accurately aligning data from different modalities within these methods. Additionally, in other point cloud-based tasks, edge point information often provides unexpected positive contributions. In this paper, we propose a novel point cloud completion method that leverages edge point information for the first time in the completion task, which also addresses the precise alignment of multi-modal data. In particular, we implement a two-step local-to-global module to achieve better alignment of multi-modal data during the preliminary point cloud generation process. Besides, we introduce a new spatial representation structure capable of extracting a fixed number of edge points. Moreover, with the assistance of edge information, we further design an inverse edge-aware upsampler to refine the point cloud. We evaluate our method on three typical datasets, and the results demonstrate that our IE-PMMA outperforms the existing state-of-the-art methods quantitatively and visually.