用空间协方差提升单图6自由度姿态估计精度。
Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation
- 用协方差池化提取特征分布的二阶统计信息
- 通过Cholesky分解构建连续姿态表示，提升鲁棒性
- 基于流形学习的端到端网络，适合部分遮挡场景
本文针对单张RGB图像的6-DoF物体姿态估计问题。间接方法通常先预测2D关键点，再用PnP求解，表现优异；而直接回归方法虽计算高效但精度较低。现有直接方法依赖全局池化特征，忽略了具有丰富姿态信息的空间二阶统计特性，且多数采用不连续的姿态表示，缺乏鲁棒性。为此，我们提出一种协方差池化表示，将卷积特征分布编码为对称正定（SPD）矩阵，并通过Cholesky分解设计新的SPD形式姿态编码。姿态回归采用考虑SPD矩阵黎曼几何的流形感知网络头，实现端到端建模。实验与消融结果一致表明，二阶池化和连续表示显著提升直接回归性能，即使在部分遮挡下亦然。
原文摘要 · Abstract (English)
In this paper, we address the problem of 6-DoF object pose estimation from a single RGB image. Indirect methods that typically predict intermediate 2D keypoints, followed by a Perspective-n-Point solver, have shown great performance. Direct approaches, which regress the pose in an end-to-end manner, are usually computationally more efficient but less accurate. However, direct pose regression heads rely on globally pooled features, ignoring spatial second-order statistics despite their informativeness in pose prediction. They also predict, in most cases, discontinuous pose representations that lack robustness. Herein, we therefore propose a covariance-pooled representation that encodes convolutional feature distributions as a symmetric positive definite (SPD) matrix. Moreover, we propose a novel pose encoding in the form of an SPD matrix via its Cholesky decomposition. Pose is then regressed in an end-to-end manner with a manifold-aware network head, taking into account the Riemannian geometry of SPD matrices. Experiments and ablations consistently demonstrate the relevance of second-order pooling and continuous representations for direct pose regression, including under partial occlusion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。