PDF(Pubmed)
Abstract：
Recent advancements in 3D modeling have revolutionized various fields, including virtual reality, computer-aided diagnosis, and architectural design, emphasizing the importance of accurate quality assessment for 3D point clouds. As these models undergo operations such as simplification and compression, introducing distortions can significantly impact their visual quality. There is a growing need for reliable and efficient objective quality evaluation methods to address this challenge. In this context, this paper introduces a novel methodology to assess the quality of 3D point clouds using a deep learning-based no-reference (NR) method. First, it extracts geometric and perceptual attributes from distorted point clouds and represent them as a set of 1D vectors. Then, transfer learning is applied to obtain high-level features using a 1D convolutional neural network (1D CNN) adapted from 2D CNN models through weight conversion from ImageNet. Finally, quality scores are predicted through regression utilizing fully connected layers. The effectiveness of the proposed approach is evaluated across diverse datasets, including the Colored Point Cloud Quality Assessment Database (SJTU_PCQA), the Waterloo Point Cloud Assessment Database (WPC), and the Colored Point Cloud Quality Assessment Database featured at ICIP2020. The outcomes reveal superior performance compared to several competing methodologies, as evidenced by enhanced correlation with average opinion scores.
摘要：
3D建模的最新进展彻底改变了各个领域，包括虚拟现实,计算机辅助诊断,和建筑设计，强调对三维点云进行准确质量评估的重要性。当这些模型经历简化和压缩等操作时，引入扭曲会显著影响他们的视觉质量。越来越需要可靠和有效的客观质量评估方法来应对这一挑战。在这种情况下，本文介绍了一种使用基于深度学习的无参考（NR）方法评估3D点云质量的新方法。首先,它从扭曲的点云中提取几何和感知属性，并将它们表示为一组一维向量。然后，通过ImageNet的权重转换，使用从2DCNN模型改编的1D卷积神经网络（1DCNN）应用迁移学习来获得高级特征。最后,质量分数是通过利用全连接层的回归预测的。所提出的方法的有效性在不同的数据集进行评估，包括彩色点云质量评估数据库(SJTU_PCQA)，滑铁卢点云评估数据库(WPC)，以及ICIP2020上的彩色点云质量评估数据库。与几种相互竞争的方法相比，结果揭示了卓越的性能，与平均意见得分的相关性增强证明了这一点。
