基于深度学习的点云压缩:深入的调查和基准
概括
深度学习显著增强了点云压缩 (PCC),克服了传统方法的局限性. 本综述系统地分析了深入的PCC算法,数据集和标准,确定了有效处理3D数据的未来研究方向.
科学领域:
- 计算机视觉 计算机视觉
- 数据压缩数据压缩
- 机器学习 机器学习
背景情况:
- 3D点云数据的爆炸性增长使存储和传输变得困难.
- 传统的混合点云压缩 (PCC) 方法与性能限制作斗争.
- 基于深度学习的PCC提供了一个有前途的解决方案来提高压缩效率.
研究的目的:
- 提供基于深度学习的点云压缩 (PCC) 的系统审查.
- 在深度PCC中分析算法演变,数据集和国际标准.
- 确定该领域的挑战和未来的研究方向.
主要方法:
- 综述流行点云数据集及其属性.
- 对有损失和无损失深度PCC的算法演化的分析.
- 针对PCC相关的MPEG和JPEG标准的调查.
- 在多个数据集上对代表性深度PCC方法进行实验性比较.
主要成果:
- 现有的深度PCC方法的全面比较,突出其优缺点.
- 确定当前深度PCC格局中的关键趋势和挑战.
- 对各种深度学习方法对PCC的有效性的实证验证.
结论:
- 深度学习对于推进点云压缩至关重要.
- 需要对深度PCC有系统的理解,以指导未来的研究.
- 未来的工作应该专注于解决更有效的3D数据压缩所面临的挑战.
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