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Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation

Haowei Wang, Jiji Tang, Jiayi Ji, Xiaoshuai Sun, Rongsheng Zhang, Yiwei Ma, Minda Zhao, Lincheng Li, zeng zhao, Tangjie Lv, Rongrong Ji

TL;DR

This work tackles the limitation of transferring 2D vision-language alignment to 3D representation learning by introducing JM3D, which unifies point clouds with multi-view images and hierarchical text. It presents Structured Multimodal Organizer (SMO) to enrich visual and textual signals via Continuous Image Sequence and Hierarchical Text Tree, and Joint Multi-modal Alignment (JMA) to model the joint distribution $P(C|I,T)$ for cohesive cross-modal learning. The approach yields state-of-the-art zero-shot 3D classification on ModelNet40 and ScanObjectNN across multiple backbones, with notable gains over prior methods, and ablations validate the contributions of CIS, HTT, and JMA. The results underscore the practical impact of joint modality modeling for robust 3D understanding and cross-modal retrieval, with code and models publicly available.

Abstract

In recent years, 3D understanding has turned to 2D vision-language pre-trained models to overcome data scarcity challenges. However, existing methods simply transfer 2D alignment strategies, aligning 3D representations with single-view 2D images and coarse-grained parent category text. These approaches introduce information degradation and insufficient synergy issues, leading to performance loss. Information degradation arises from overlooking the fact that a 3D representation should be equivalent to a series of multi-view images and more fine-grained subcategory text. Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality. In this paper, we propose a multi-view joint modality modeling approach, termed JM3D, to obtain a unified representation for point cloud, text, and image. Specifically, a novel Structured Multimodal Organizer (SMO) is proposed to address the information degradation issue, which introduces contiguous multi-view images and hierarchical text to enrich the representation of vision and language modalities. A Joint Multi-modal Alignment (JMA) is designed to tackle the insufficient synergy problem, which models the joint modality by incorporating language knowledge into the visual modality. Extensive experiments on ModelNet40 and ScanObjectNN demonstrate the effectiveness of our proposed method, JM3D, which achieves state-of-the-art performance in zero-shot 3D classification. JM3D outperforms ULIP by approximately 4.3% on PointMLP and achieves an improvement of up to 6.5% accuracy on PointNet++ in top-1 accuracy for zero-shot 3D classification on ModelNet40. The source code and trained models for all our experiments are publicly available at https://github.com/Mr-Neko/JM3D.

Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation

TL;DR

This work tackles the limitation of transferring 2D vision-language alignment to 3D representation learning by introducing JM3D, which unifies point clouds with multi-view images and hierarchical text. It presents Structured Multimodal Organizer (SMO) to enrich visual and textual signals via Continuous Image Sequence and Hierarchical Text Tree, and Joint Multi-modal Alignment (JMA) to model the joint distribution for cohesive cross-modal learning. The approach yields state-of-the-art zero-shot 3D classification on ModelNet40 and ScanObjectNN across multiple backbones, with notable gains over prior methods, and ablations validate the contributions of CIS, HTT, and JMA. The results underscore the practical impact of joint modality modeling for robust 3D understanding and cross-modal retrieval, with code and models publicly available.

Abstract

In recent years, 3D understanding has turned to 2D vision-language pre-trained models to overcome data scarcity challenges. However, existing methods simply transfer 2D alignment strategies, aligning 3D representations with single-view 2D images and coarse-grained parent category text. These approaches introduce information degradation and insufficient synergy issues, leading to performance loss. Information degradation arises from overlooking the fact that a 3D representation should be equivalent to a series of multi-view images and more fine-grained subcategory text. Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality. In this paper, we propose a multi-view joint modality modeling approach, termed JM3D, to obtain a unified representation for point cloud, text, and image. Specifically, a novel Structured Multimodal Organizer (SMO) is proposed to address the information degradation issue, which introduces contiguous multi-view images and hierarchical text to enrich the representation of vision and language modalities. A Joint Multi-modal Alignment (JMA) is designed to tackle the insufficient synergy problem, which models the joint modality by incorporating language knowledge into the visual modality. Extensive experiments on ModelNet40 and ScanObjectNN demonstrate the effectiveness of our proposed method, JM3D, which achieves state-of-the-art performance in zero-shot 3D classification. JM3D outperforms ULIP by approximately 4.3% on PointMLP and achieves an improvement of up to 6.5% accuracy on PointNet++ in top-1 accuracy for zero-shot 3D classification on ModelNet40. The source code and trained models for all our experiments are publicly available at https://github.com/Mr-Neko/JM3D.
Paper Structure (32 sections, 12 equations, 3 figures, 10 tables)

This paper contains 32 sections, 12 equations, 3 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Representation Learning in 3D Space
  4. Representation Learning in Multi-modal Space
  5. Learning 3D Representation with Multi-modal
  6. Method
  7. Preliminary
  8. Structured Multi-modal Organizer
  9. Continuous Image Sequence
  10. Hierarchical Text Tree
  11. Joint Multi-modal Alignment
  12. Training Objective
  13. Experiment
  14. Datasets
  15. Pretrain Datasets
  16. ...and 17 more sections

Figures (3)

  • Figure 1: The visualization of JM3D. JM3D aligns 3D modality with the pre-aligned vision and language modalities, constructing a unified representation of the three modalities. Continuous Image Sequence (CIS, left) and Hierarchical Text Tree (HTT, right) organize structured images and texts to enhance the information from vision and language modalities. The joint alignment and modeling (green line) correct the inappropriate way of independent alignment (red line) used in previous methods.
  • Figure 2: The framework of JM3D. Continuous Image Sequence (CIS) and Hierarchical Text Tree (HTT) organized continuous multi-view images and hierarchical texts respectively, which are fed into a pre-training model (frozen) to extract features on the left. Then, Joint Multi-modal Alignment (JMA) incorporates the features from two modalities to generate the joint modeling features. On the last, contrastive learning is applied to align 3D features (training) with joint features and subcategory texts, while 3D features are aggregated with the assistance of the parent category.
  • Figure 3: The qualitative results of the real image to point cloud retrieval. Giving an image, We show the top-3 point cloud retrieval results from ModelNet40. All models perform well on the simple samples (the 1st row and the 3rd row). However, when it comes to the challenging samples (the 2nd row and the 4th row), JM3D demonstrates a more accurate retrieval ability compared to the previous state-of-the-art (ULIP). The JM3D trained with 4 view images shows better performance compared with the 2 view images, benefiting the more solid bias of vision modality.