Reconstruction of 3D open surfaces (e.g., non-watertight meshes) is an underexplored area of computer vision. Recent learning-based implicit techniques have removed previous barriers by enabling reconstruction in arbitrary resolutions. Yet, such approaches often rely on distinguishing between the inside and outside of a surface in order to extract a zero level set when reconstructing the target. In the case of open surfaces, this distinction often leads to artifacts such as the artificial closing of surface gaps. However, real-world data may contain intricate details defined by salient surface gaps. Implicit functions that regress an unsigned distance field have shown promise in reconstructing such open surfaces. Nonetheless, current unsigned implicit methods rely on a discretized representation of the raw data. This not only bounds the learning process to the representation's resolution, but it also introduces outliers in the reconstruction. To enable accurate reconstruction of open surfaces without introducing outliers, we propose a learning-based implicit point-voxel model (IPVNet). IPVNet predicts the unsigned distance between a surface and a query point in 3D space by leveraging both raw point cloud data and its discretized voxel counterpart. Experiments on synthetic and real-world public datasets demonstrates that IPVNet outperforms the state of the art while producing far fewer outliers in the resulting reconstruction.

翻译：三维开放曲面（如非水密网格）的重建是计算机视觉中一个探索不足的领域。近年来基于学习的隐式技术通过实现任意分辨率下的重建，消除了以往的技术障碍。然而，这类方法通常依赖区分曲面内外侧以提取零水平集来重建目标。对于开放曲面而言，这种区分常导致曲面间隙被人工闭合等伪影。但真实世界数据可能包含由显著曲面间隙定义的复杂细节。回归无符号距离场的隐式函数在重建此类开放曲面方面展现出潜力。然而，当前无符号隐式方法依赖于原始数据的离散化表示，这不仅将学习过程限制在表示分辨率范围内，还会在重建中引入异常点。为在无异常点前提下实现开放曲面的精确重建，本文提出一种基于学习的隐式点-体素模型IPVNet。该模型通过融合原始点云数据及其离散化体素对应数据，预测三维空间中曲面与查询点之间的无符号距离。在合成与真实公共数据集上的实验表明，IPVNet在显著减少重建结果中异常点数量的同时，性能超越了现有最先进方法。