联合条件立体表面推理用于交互场估计
Joint-Conditioned Stereo Surface Reasoning for Interaction Field Estimation
- Ant Digital Technologies, Ant Group（蚂蚁集团蚂蚁数字科技）
- Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出联合条件立体表面推理（JSSR）方法，通过时间-立体网络联合预测3D关节和交互场，利用候选搜索与共享表面证据，在SHOW3D挑战中排名第三。
AI中文摘要：
预测手-物体交互场需要为每个手部关节定位最近的物体表面点，这通常涉及小而部分遮挡的图像区域。我们将此任务视为联合条件表面端点估计：每个关节有其自身的最近端点，而来自同一只手的端点可以共享局部表面证据。这一结构启发了联合条件立体表面推理（JSSR）。一个时间-立体网络联合预测3D关节、直接交互场以及每视角端点证据。校准的候选搜索利用关节特定图像兼容性和跨视角对应性来评估端点假设。手共享候选支持使关节能够利用共同表面证据，并且一个学习到的残差门控制在观测模糊时进行几何校正。基于此方法构建的系统在SHOW3D交互场挑战排行榜上排名第三。
英文摘要：
Predicting hand--object interaction fields requires locating the nearest object-surface point for each hand joint, often from small and partially occluded image regions. We view this task as joint-conditioned surface-endpoint estimation: each joint has its own nearest endpoint, while endpoints from the same hand can draw on shared local surface evidence. This structure motivates Joint-Conditioned Stereo Surface Reasoning (JSSR). A temporal-stereo network jointly predicts 3D joints, a direct interaction field, and per-view endpoint evidence. Calibrated candidate search evaluates endpoint hypotheses using joint-specific image compatibility and cross-view correspondence. A hand-shared candidate support lets joints draw on common surface evidence, and a learned residual gate controls the geometric correction when observations are ambiguous. Our system built on this method ranked third on the SHOW3D Interaction Field Challenge leaderboard.
