Relative Pose Estimation for RGB-D Human Input Scans via Human Completion

Pengpeng Liu, Guixuan Zhang, Hu Guan, Jie Liu, Shuwu Zhang, Zhi Zengi
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Abstract

Relative pose estimation for human scans enjoys a promising prospect. However, most existing methods mainly focus on indoor or outdoor scenes, requiring considerable overlap between the inputs. We present a technique for estimating the relative pose whatever the overlap between the human RGB- D input scans is. For non-overlapping scans, the insight is to take advantage of the underlying human geometry prior as much as possible. We utilize the implicit function model for human reconstruction, enriching abundant hidden cues for unseen regions, then we use the completed human geometry to get a stable pose estimation. Our evaluation shows that our approach outperforms considerably than standard pipelines in non-overlapping setting, without compromising performance over overlapping input scans.
基于人工补全的RGB-D人工输入扫描的相对姿态估计
人体扫描的相对姿态估计具有广阔的应用前景。然而,大多数现有的方法主要集中在室内或室外场景,要求输入之间有相当大的重叠。我们提出了一种估计相对姿态的技术,无论人类RGB- D输入扫描之间的重叠是多少。对于非重叠扫描,我们的想法是尽可能地利用潜在的人体几何结构。我们利用隐式函数模型进行人体重建,为未见区域丰富了丰富的隐藏线索,然后利用完整的人体几何图形得到稳定的姿态估计。我们的评估表明,我们的方法在非重叠设置下比标准管道性能要好得多,并且不会影响重叠输入扫描的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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