As 3D point clouds become a cornerstone of modern technology, the need for sophisticated generative models and reliable evaluation metrics has grown exponentially. In this work, we first expose that some commonly used metrics for evaluating generated point clouds, particularly those based on Chamfer Distance (CD), lack robustness against defects and fail to capture geometric fidelity and local shape consistency when used as quality indicators. We further show that introducing samples alignment prior to distance calculation and replacing CD with Density-Aware Chamfer Distance (DCD) are simple yet essential steps to ensure the consistency and robustness of point cloud generative model evaluation metrics. While existing metrics primarily focus on directly comparing 3D Euclidean coordinates, we present a novel metric, named Surface Normal Concordance (SNC), which approximates surface similarity by comparing estimated point normals. This new metric, when combined with traditional ones, provides a more comprehensive evaluation of the quality of generated samples. Finally, leveraging recent advancements in transformer-based models for point cloud analysis, such as serialized patch attention , we propose a new architecture for generating high-fidelity 3D structures, the Diffusion Point Transformer. We perform extensive experiments and comparisons on the ShapeNet dataset, showing that our model outperforms previous solutions, particularly in terms of quality of generated point clouds, achieving new state-of-the-art. Code available at https://github.com/matteo-bastico/DiffusionPointTransformer.

翻译：随着三维点云成为现代技术的基石，对复杂生成模型和可靠评估指标的需求呈指数级增长。在本研究中，我们首先揭示了一些常用于评估生成点云的指标，特别是那些基于Chamfer距离（CD）的指标，在作为质量指标时缺乏对缺陷的鲁棒性，且未能捕捉几何保真度和局部形状一致性。我们进一步表明，在距离计算前引入样本对齐，并用密度感知Chamfer距离（DCD）替代CD，是确保点云生成模型评估指标一致性和鲁棒性的简单而关键的步骤。现有指标主要侧重于直接比较三维欧几里得坐标，而我们提出了一种名为表面法向一致性（SNC）的新指标，通过比较估计的点法向来近似表面相似性。这一新指标与传统指标结合使用时，能对生成样本的质量提供更全面的评估。最后，利用基于Transformer的点云分析模型（如序列化补丁注意力）的最新进展，我们提出了一种用于生成高保真三维结构的新架构——扩散点Transformer。我们在ShapeNet数据集上进行了大量实验和比较，结果表明我们的模型优于先前解决方案，特别是在生成点云的质量方面，达到了新的最先进水平。代码可在https://github.com/matteo-bastico/DiffusionPointTransformer获取。