用离散-连续分步法提升6D姿态估计精度与速度
Flow6D: Discrete-to-Continuous Flow Matching for Efficient and Accurate Category-Level 6D Pose Estimation
- 先离散定位再连续优化，降低搜索空间复杂度
- 真实场景下达70帧每秒，精度超越现有方法
- 适用于刚性与可动物体，适合机器人抓取应用
6D姿态估计是计算机视觉和具身智能中的关键任务，广泛应用于机器人操作、增强现实等场景。现有方法直接在高维连续空间中回归，面临两大挑战：噪声与局部最优导致精度受限，无限搜索空间影响实时性能。本文提出Flow6D，一种分层流匹配框架，采用两阶段策略：先将旋转和平移参数离散化为桶，通过离散流匹配模型将潜在空间锁定在真实姿态附近，显著降低搜索复杂度；随后在潜在空间采样，利用连续流匹配模型预测局部姿态残差，优化并精确回归姿态。该框架自然扩展至可动物体，在合成与真实数据集上均优于现有最先进方法，实现70 FPS的实时推理。项目网站：https://flow6d.github.io/。
原文摘要 · Abstract (English)
6D pose estimation is a key task in computer vision and embodied AI, widely used in robotic manipulation, augmented reality, etc. Existing methods directly regress in a high-dimensional continuous space, facing two key challenges in category-level pose estimation: limited accuracy due to noise and local optima, and inefficient search over an infinite space that hinders real-time performance. This paper proposes Flow6D, a hierarchical flow matching framework with a two-stage discrete latent space localization-continuous pose regression strategy. Rotation and translation parameters are first discretized into bins, with a discrete flow matching model locking the latent space around the true pose to reduce search complexity. Then, by sampling in the latent space, a continuous flow matching model predicts local pose residuals to optimize the estimate and regress to an accurate pose. The framework also naturally extends to articulated objects, outperforming state-of-the-art methods on synthetic and real datasets with real-time inference at 70 FPS. Project website: https://flow6d.github.io/.
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