CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds

2025-01-07

AI Summary

To address the coarse-grained point cloud representation and ambiguous cross-modal alignment in 3D multimodal large models, this paper proposes a contrastive learning framework based on high-resolution point clouds. Methodologically, we introduce (1) the statistical odds ratio—novelly applied to 3D multimodal contrastive learning—as an auxiliary loss term to enhance fine-grained semantic discrimination; and (2) a high-density adaptive point cloud sampling strategy coupled with a semantically explicit hard negative sample generation mechanism to improve vision–text alignment fidelity. Evaluated on comprehensive 3D scene understanding and reasoning benchmarks, our approach achieves state-of-the-art performance. Ablation studies confirm that both high-resolution point cloud modeling and the odds ratio loss are critical contributors to performance gains, validating their effectiveness in refining representation granularity and strengthening cross-modal semantic consistency.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Recent research has demonstrated that Large Language Models (LLMs) are not limited to text-only tasks but can also function as multimodal models across various modalities, including audio, images, and videos. In particular, research on 3D Large Multimodal Models (3D LMMs) is making notable strides, driven by the potential of processing higher-dimensional data like point clouds. However, upon closer examination, we find that the visual and textual content within each sample of existing training datasets lacks both high informational granularity and clarity, which serve as a bottleneck for precise cross-modal understanding. To address these issues, we propose CL3DOR, Contrastive Learning for 3D large multimodal models via Odds ratio on high-Resolution point clouds, designed to ensure greater specificity and clarity in both visual and textual content. Specifically, we increase the density of point clouds per object and construct informative hard negative responses in the training dataset to penalize unwanted responses. To leverage hard negative responses, we incorporate the odds ratio as an auxiliary term for contrastive learning into the conventional language modeling loss. CL3DOR achieves state-of-the-art performance in 3D scene understanding and reasoning benchmarks. Additionally, we demonstrate the effectiveness of CL3DOR's key components through extensive experiments.

Problem

• 3D Multi-modal Models
• Point Cloud Data
• Information Understanding Limitations

Innovation

• CL3DOR
• Dense Point Cloud Data
• Odd Ratio Approach