HOISDF：利用全局符号距离场约束三维手物姿态估计
HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
AI总结：
本文提出HOISDF网络，利用手部和物体的全局符号距离场作为隐式形状先验，从单目图像中联合估计手物三维姿态，在DexYCB和HO3Dv2基准上达到最先进性能。
摘要：
人手具有高度关节化结构，在操作物体时表现出极强的灵活性。由于频繁的遮挡，从单目相机联合估计手部及其所操作物体的三维姿态极具挑战性。因此，现有方法通常依赖中间三维形状表示来提升性能。这些表示通常是显式的，例如三维点云或网格，因而只能在中间手部姿态估计值的直接邻域内提供信息。为解决这一问题，我们提出了HOISDF，一种以符号距离场（SDF）引导的手物姿态估计网络，该网络联合利用手部和物体的SDF，在整个重建体上提供全局的隐式表示。具体而言，SDF的作用有三方面：为视觉编码器配备隐式形状信息，帮助编码手物交互关系，并通过基于SDF的采样和增强特征表示来引导手部和物体的姿态回归。我们证明HOISDF在手物姿态估计基准（DexYCB和HO3Dv2）上取得了最先进的结果。代码可在https://github.com/amathislab/HOISDF获取。
英文摘要：
Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus, existing methods often rely on intermediate 3D shape representations to increase performance. These representations are typically explicit, such as 3D point clouds or meshes, and thus provide information in the direct surroundings of the intermediate hand pose estimate. To address this, we introduce HOISDF, a Signed Distance Field (SDF) guided hand-object pose estimation network, which jointly exploits hand and object SDFs to provide a global, implicit representation over the complete reconstruction volume. Specifically, the role of the SDFs is threefold: equip the visual encoder with implicit shape information, help to encode hand-object interactions, and guide the hand and object pose regression via SDF-based sampling and by augmenting the feature representations. We show that HOISDF achieves state-of-the-art results on hand-object pose estimation benchmarks (DexYCB and HO3Dv2). Code is available at https://github.com/amathislab/HOISDF
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
