Occupancy Planes for Single-view RGB-D Human Reconstruction-猫眼课题宝

DOI：

10.48550/arxiv.2208.02817

发表时间：

2022-08

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Xiaoming Zhao;Yuan-Ting Hu;Zhongzheng Ren;A. Schwing

具有隐函数的单视图RGB-D人体重建通常被公式化为逐点分类。具体地，首先将相机的视锥体内的一组3D位置独立地投影到图像上，并且随后针对每个3D位置提取对应的特征。然后使用每个3D位置的特征来独立分类相应的3D点是在观察对象的内部还是外部。该过程导致次优结果，因为相邻位置的预测之间的相关性仅通过所提取的特征被隐式地考虑。为了获得更准确的结果，我们提出了占用平面（OPlanes）表示，这使得能够将单视图RGB-D人体重建制定为切片通过相机视锥的平面上的占用预测。这种表示提供了比体素网格更大的灵活性，并且能够比逐点分类更好地利用相关性。在具有挑战性的S3 D数据上，我们观察到一个基于OPlanes表示的简单分类器，以产生令人信服的结果，特别是在由于其他对象和部分可见性而导致部分遮挡的困难情况下，这些问题尚未在之前的工作中得到解决。

Single-view RGB-D human reconstruction with implicit functions is often formulated as per-point classification. Specifically, a set of 3D locations within the view-frustum of the camera are first projected independently onto the image and a corresponding feature is subsequently extracted for each 3D location. The feature of each 3D location is then used to classify independently whether the corresponding 3D point is inside or outside the observed object. This procedure leads to sub-optimal results because correlations between predictions for neighboring locations are only taken into account implicitly via the extracted features. For more accurate results we propose the occupancy planes (OPlanes) representation, which enables to formulate single-view RGB-D human reconstruction as occupancy prediction on planes which slice through the camera's view frustum. Such a representation provides more flexibility than voxel grids and enables to better leverage correlations than per-point classification. On the challenging S3D data we observe a simple classifier based on the OPlanes representation to yield compelling results, especially in difficult situations with partial occlusions due to other objects and partial visibility, which haven't been addressed by prior work.

Occupancy Planes for Single-view RGB-D Human Reconstruction

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