Multi-modal Multi-task Pre-training for Improved Point Cloud Understanding

2025-07-23

AI Summary

Existing 3D multimodal pre-training methods typically rely on a single pretext task, limiting their ability to fully exploit cross-modal complementary information and thereby constraining generalization to downstream tasks. To address this, we propose MMPT—a multimodal multi-task pre-training framework that, for the first time, unifies token-level reconstruction, point-level reconstruction, and cross-modal contrastive learning within unsupervised point cloud understanding. MMPT jointly optimizes representations for both 3D point clouds and 2D images without requiring any 3D annotations, significantly enhancing feature discriminability and transferability. Extensive experiments demonstrate that MMPT consistently outperforms state-of-the-art methods across diverse downstream tasks—including classification, segmentation, and generation—on major benchmarks such as ModelNet40, ScanObjectNN, and ShapeNet. These results validate the effectiveness and generality of multi-task collaborative modeling for multimodal point cloud representation learning.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advances in multi-modal pre-training methods have shown promising effectiveness in learning 3D representations by aligning multi-modal features between 3D shapes and their corresponding 2D counterparts. However, existing multi-modal pre-training frameworks primarily rely on a single pre-training task to gather multi-modal data in 3D applications. This limitation prevents the models from obtaining the abundant information provided by other relevant tasks, which can hinder their performance in downstream tasks, particularly in complex and diverse domains. In order to tackle this issue, we propose MMPT, a Multi-modal Multi-task Pre-training framework designed to enhance point cloud understanding. Specifically, three pre-training tasks are devised: (i) Token-level reconstruction (TLR) aims to recover masked point tokens, endowing the model with representative learning abilities. (ii) Point-level reconstruction (PLR) is integrated to predict the masked point positions directly, and the reconstructed point cloud can be considered as a transformed point cloud used in the subsequent task. (iii) Multi-modal contrastive learning (MCL) combines feature correspondences within and across modalities, thus assembling a rich learning signal from both 3D point cloud and 2D image modalities in a self-supervised manner. Moreover, this framework operates without requiring any 3D annotations, making it scalable for use with large datasets. The trained encoder can be effectively transferred to various downstream tasks. To demonstrate its effectiveness, we evaluated its performance compared to state-of-the-art methods in various discriminant and generative applications under widely-used benchmarks.

Problem

• Enhance 3D point cloud understanding via multi-task learning
• Address limited information from single pre-training tasks
• Improve performance in complex downstream applications

Innovation

• Multi-modal multi-task pre-training for point clouds
• Token-level and point-level reconstruction tasks
• Self-supervised multi-modal contrastive learning