用于G-PCC压缩LiDAR的后处理几何增强通过圆柱形密集.
概括
这项研究引入了一种新的后处理方法来增强压缩的LiDAR点云 (CLGE),显著提高几何质量并减少低位数压缩的工件.
科学领域:
- 计算机视觉 计算机视觉
- 信号处理 信号处理
- 激光雷达技术 (LiDAR) 是一种技术.
背景情况:
- 基于几何学的点云压缩算法高效地传输稀疏的LiDAR数据.
- 低位压缩引入了严重的几何工件,包括点缩小和坐标偏移.
- 现有的方法缺乏用于直接增强压缩LiDAR点云的几何形状的后处理解决方案.
研究的目的:
- 开发一种新的后处理方法,以提高压缩LiDAR点云 (CLGE) 的几何质量.
- 为解决由低位压缩模式引起的几何压缩文物.
- 为了提高下游任务的性能,例如受压缩扭曲影响的对象检测.
主要方法:
- 一种双分支的方法:使用多尺度稀疏卷曲的圆柱形密度化和使用MLP层的自适应性精细化.
- 圆柱形密度化采用大型不对称的稀疏卷积核用于特征提取和候选点生成.
- 适应性改进利用邻近特征来改进候选点,然后进行基于环的最远点重新采样,以保存几何分布.
主要成果:
- 拟议的方法有效地提高了各种文物级别的压缩LiDAR点云 (CLGE) 的几何质量.
- 压缩诱导的几何文物显著减少,包括点减少和坐标偏移.
- 增强的点云可以缓解对象检测任务的性能下降.
结论:
- 开发的后处理方法为改善CLGE几何质量提供了直接的解决方案.
- 该方法是多功能,可扩展到上采样,并且对噪声强大.
- 对CLGE的几何增强可以提高视觉真实性和下游任务性能.
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