CLIP-PCQA: 探索面向点云质量评估的主观对齐视觉语言建模
CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment
AI总结：
针对现有无参考点云质量评估方法直接映射得分不符合主观评估机制的问题，提出CLIP-PCQA方法，通过检索式映射、对比损失与可学习提示，以意见得分分布为目标实现更优的主观对齐评估效果。
摘要：
近年来，无参考点云质量评估（NR-PCQA）研究取得了显著进展。然而，现有方法大多寻求从视觉数据到平均意见得分（MOS）的直接映射函数，这与实际主观评估的机制相矛盾。为解决该问题，我们提出了一种名为CLIP-PCQA的新型语言驱动PCQA方法。考虑到人类更倾向于使用离散质量描述（例如“优秀”和“较差”）而非具体分数来描述视觉质量，我们采用基于检索的映射策略来模拟主观评估过程。具体而言，基于CLIP的理念，我们计算视觉特征与对应不同质量描述的多个文本特征之间的余弦相似度，在此过程中引入了有效的对比损失和可学习提示以增强特征提取。同时，鉴于主观实验中存在的个人局限性和偏差，我们进一步将特征相似度转换为概率，并将意见得分分布（OSD）而非单个MOS作为最终目标。实验结果表明，我们的CLIP-PCQA性能优于其他现有最先进（SOTA）方法。
英文摘要：
In recent years, No-Reference Point Cloud Quality Assessment (NR-PCQA) research has achieved significant progress. However, existing methods mostly seek a direct mapping function from visual data to the Mean Opinion Score (MOS), which is contradictory to the mechanism of practical subjective evaluation. To address this, we propose a novel language-driven PCQA method named CLIP-PCQA. Considering that human beings prefer to describe visual quality using discrete quality descriptions (e.g., "excellent" and "poor") rather than specific scores, we adopt a retrieval-based mapping strategy to simulate the process of subjective assessment. More specifically, based on the philosophy of CLIP, we calculate the cosine similarity between the visual features and multiple textual features corresponding to different quality descriptions, in which process an effective contrastive loss and learnable prompts are introduced to enhance the feature extraction. Meanwhile, given the personal limitations and bias in subjective experiments, we further covert the feature similarities into probabilities and consider the Opinion Score Distribution (OSD) rather than a single MOS as the final target. Experimental results show that our CLIP-PCQA outperforms other State-Of-The-Art (SOTA) approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
