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GT-PCQA: Geometry-Texture Decoupled Point Cloud Quality Assessment with MLLM

Guohua Zhang, Jian Jin, Meiqin Liu, Chao Yao, Weisi Lin, Yao Zhao

Abstract

With the rapid advancement of Multi-modal Large Language Models (MLLMs), MLLM-based Image Quality Assessment (IQA) methods have shown promising generalization. However, directly extending these MLLM-based IQA methods to PCQA remains challenging. On the one hand, existing PCQA datasets are limited in scale, which hinders stable and effective instruction tuning of MLLMs. On the other hand, due to large-scale image-text pretraining, MLLMs tend to rely on texture-dominant reasoning and are insufficiently sensitive to geometric structural degradations that are critical for PCQA. To address these gaps, we propose a novel MLLM-based no-reference PCQA framework, termed GT-PCQA, which is built upon two key strategies. First, to enable stable and effective instruction tuning under scarce PCQA supervision, a 2D-3D joint training strategy is proposed. This strategy formulates PCQA as a relative quality comparison problem to unify large-scale IQA datasets with limited PCQA datasets. It incorporates a parameter-efficient Low-Rank Adaptation (LoRA) scheme to support instruction tuning. Second, a geometry-texture decoupling strategy is presented, which integrates a dual-prompt mechanism with an alternating optimization scheme to mitigate the inherent texture-dominant bias of pre-trained MLLMs, while enhancing sensitivity to geometric structural degradations. Extensive experiments demonstrate that GT-PCQA achieves competitive performance and exhibits strong generalization.

GT-PCQA: Geometry-Texture Decoupled Point Cloud Quality Assessment with MLLM

Abstract

With the rapid advancement of Multi-modal Large Language Models (MLLMs), MLLM-based Image Quality Assessment (IQA) methods have shown promising generalization. However, directly extending these MLLM-based IQA methods to PCQA remains challenging. On the one hand, existing PCQA datasets are limited in scale, which hinders stable and effective instruction tuning of MLLMs. On the other hand, due to large-scale image-text pretraining, MLLMs tend to rely on texture-dominant reasoning and are insufficiently sensitive to geometric structural degradations that are critical for PCQA. To address these gaps, we propose a novel MLLM-based no-reference PCQA framework, termed GT-PCQA, which is built upon two key strategies. First, to enable stable and effective instruction tuning under scarce PCQA supervision, a 2D-3D joint training strategy is proposed. This strategy formulates PCQA as a relative quality comparison problem to unify large-scale IQA datasets with limited PCQA datasets. It incorporates a parameter-efficient Low-Rank Adaptation (LoRA) scheme to support instruction tuning. Second, a geometry-texture decoupling strategy is presented, which integrates a dual-prompt mechanism with an alternating optimization scheme to mitigate the inherent texture-dominant bias of pre-trained MLLMs, while enhancing sensitivity to geometric structural degradations. Extensive experiments demonstrate that GT-PCQA achieves competitive performance and exhibits strong generalization.
Paper Structure (14 sections, 7 equations, 1 figure, 4 tables)

This paper contains 14 sections, 7 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. 2D–3D Joint Training Strategy
  4. Geometry–Texture Decoupling Strategy
  5. Dual-Prompt Mechanism
  6. Alternating Optimization Scheme
  7. Experiments
  8. Experimental Setup
  9. Dataset and Evaluation Metrics
  10. Implementation Details
  11. Performance Evaluation
  12. Ablation Study
  13. Cross-Dataset Validation
  14. Conclusion

Figures (1)

  • Figure 1: Architecture of the proposed GT-PCQA. (a) During the training stage, the model alternates between image pairs and multi-view point cloud pairs. Visual inputs are encoded by the vision encoder and abstracted into compact representations, while attribute-specific text prompts (e.g., texture-aware or geometry-aware) are embedded into textual representations. The aligned multimodal features are then fed into a LoRA-adapted LLM to perform relative quality comparison, enabling stable and effective instruction tuning under heterogeneous 2D–3D supervision. (b) During the inference stage, the trained MLLM is fully frozen, and each test point cloud is evaluated against an anchor set via soft comparison, ultimately predicting the final quality score.