事件点网:仅从事件点云中恢复人类网格
IEEE transactions on visualization and computer graphics
|September 18, 2024
概括
本研究仅使用事件摄像头数据来探索人体网状恢复,绕过传统强度图像. 拟议的EventPointMesh框架成功地从稀疏的事件数据中重建人类形状,优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 神经形态工程的神经形态工程
- 人类形状分析 人类形状分析
背景情况:
- 传统的人类网格恢复 (HMR) 依赖于强度图像,这些图像容易受到照明变化,隐私问题和高资源需求的影响.
- 事件摄像机通过捕捉高速度的亮度变化提供了替代方案,独立于照明条件,但它们在HMR中的应用尚未得到充分探索.
研究的目的:
- 调查仅使用事件摄像头数据重建人类形状的可行性.
- 为HMR开发一个新的框架,EventPointMesh,克服了基于强度的方法的局限性.
主要方法:
- 拟议的EventPointMesh框架将事件数据视为3D时空点云.
- 实施粗细的姿势特征提取策略,以捕捉全球和本地人体特征.
- 将分散事件点分组成对应于个体部位的细分部分,以便进行详细分析.
主要成果:
- 通过仅使用稀疏事件数据,证明了精确的人类网状复原.
- 在HMR任务中,EventPointMesh的表现优于现有的基线方法.
- 该方法通过结合全球和本地特征提取有效捕捉微妙的人类运动.
结论:
- 仅仅是事件摄像头数据就提供了足够的信息来进行强大的人体网格恢复.
- EventPointMesh为HMR提供了一种保护隐私,耐低光和高效的替代方案.
- 提出的方法推进了神经形态视觉在人类姿势和形状估计中的应用.
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