Multilayer perceptrons (MLPs) have been successfully used to represent 3D shapes implicitly and compactly, by mapping 3D coordinates to the corresponding signed distance values or occupancy values. In this paper, we propose a novel positional encoding scheme, called Spline Positional Encoding, to map the input coordinates to a high dimensional space before passing them to MLPs, for helping to recover 3D signed distance fields with fine-scale geometric details from unorganized 3D point clouds. We verified the superiority of our approach over other positional encoding schemes on tasks of 3D shape reconstruction from input point clouds and shape space learning. The efficacy of our approach extended to image reconstruction is also demonstrated and evaluated.
翻译:多层透视器(MLPs)已被成功用于隐含和缩略地代表 3D 形状, 绘制 3D 坐标与对应的签名距离值或占用值之间的对应位置值。 在本文中, 我们提出一个新的位置编码方案, 名为 Spline 定位编码, 在将其传送到 MLPs 之前, 将输入坐标映射到高维空间, 帮助从无组织 3D 点云中恢复 3D 签名的距离域, 并附上精细的几何细节 。 我们验证了我们的方法优于 3D 形状重建任务的其他位置编码方案, 包括输入点云和塑造空间学习 。 我们还演示和评价了我们用于图像重建的方法的有效性 。