[论文解读] Sampling color and geometry point clouds from ShapeNet dataset

Davi Lazzarotto, Touradj Ebrahimi|arXiv (Cornell University)|Jan 18, 2022

3D Shape Modeling and Analysis被引用 7

一句话总结

本文提出了一种脚本，通过在采样前移除具有冲突颜色的内部重复面，从ShapeNet网格中采样高质量、色彩准确的点云。利用Meshlab的环境光遮蔽和迭代面选择，确保仅采样外表面，随后借助CloudCompare和Open3D生成保留色彩、量化后的点云，相比直接采样在视觉保真度上有所提升。

ABSTRACT

The popularisation of acquisition devices capable of capturing volumetric information such as LiDAR scans and depth cameras has lead to an increased interest in point clouds as an imaging modality. Due to the high amount of data needed for their representation, efficient compression solutions are needed to enable practical applications. Among the many techniques that have been proposed in the last years, learning-based methods are receiving large attention due to their high performance and potential for improvement. Such algorithms depend on large and diverse training sets to achieve good compression performance. ShapeNet is a large-scale dataset composed of CAD models with texture and constitute and effective option for training such compression methods. This dataset is entirely composed of meshes, which must go through a sampling process in order to obtain point clouds with geometry and texture information. Although many existing software libraries are able to sample geometry from meshes through simple functions, obtaining an output point cloud with geometry and color of the external faces of the mesh models is not a straightforward process for the ShapeNet dataset. The main difficulty associated with this dataset is that its models are often defined with duplicated faces sharing the same vertices, but with different color values. This document describes a script for sampling the meshes from ShapeNet that circumvent this issue by excluding the internal faces of the mesh models prior to the sampling. The script can be accessed from the following link: https://github.com/mmspg/mesh-sampling.

研究动机与目标

• 解决由于重复且颜色冲突的面导致从ShapeNet网格直接采样时点云出现颜色失真的问题。
• 为基于学习的压缩训练提供从ShapeNet的纹理CAD模型中生成高保真度点云的能力。
• 开发一种稳健的预处理流程，在消除背面或重复面引起的视觉噪声的同时，保留几何与色彩信息。
• 提供一种可复现的开源解决方案，用于从ShapeNet网格生成干净、量化后的点云，实现一致的颜色表示。

提出的方法

• 通过利用Meshlab的环境光遮蔽插件，基于多个虚拟视角分配可见性评分，检测并移除ShapeNet网格中的内部面。
• 通过迭代识别共享相同顶点的面，仅保留可见性评分最高的面，从而有效消除具有冲突颜色的重复面。
• 使用CloudCompare对清理后的网格进行采样，生成同时保留几何与颜色属性的点云。
• 使用Open3D将生成的点云缩放至立方体包围盒并量化为均匀网格，重叠点的颜色进行平均。
• 该流程确保仅外表面且无重复的面参与最终点云的生成，最大限度减少视觉伪影。
• 整个过程通过GitHub上公开的脚本实现自动化，保证可复现性，并可在研究中重复使用。

实验结果

研究问题

• RQ1 如何自动检测并移除ShapeNet网格中的内部和重复面，以防止采样点云时出现颜色污染？
• RQ2 具有不同颜色的面重复对直接采样生成的点云视觉质量有何影响？
• RQ3 一种在保留网格拓扑的同时移除冲突面的预处理流程，能否生成更准确且视觉一致的点云，以用于基于学习的压缩？
• RQ4 与使用标准库的直接采样相比，所提出方法在颜色保真度方面提升了多少？

主要发现

• 所提出的方法通过移除具有冲突颜色的内部和重复面，成功消除了从ShapeNet网格采样点云时的视觉噪声和颜色伪影。
• 使用Meshlab的环境光遮蔽插件可可靠识别外表面，即使在复杂的网格结构中亦然。
• 使用该预处理流程生成的点云在视觉一致性与颜色准确性方面显著优于直接采样方法。
• 最终输出为经过量化、均匀网格化的点云，重叠位置的颜色已平均化，适用于深度学习驱动的压缩模型训练。
• 该方法在不改变原始网格结构的前提下，实现了高保真度的颜色与几何表示，仅移除了不可见且存在冲突的面。
• 开源脚本使得ShapeNet数据集的点云压缩研究中可复现且可扩展的预处理成为可能。