用条件流匹配生成6D位姿，解决对称与特征缺失问题
Generative 6D Pose Estimation via Conditional Flow Matching
- 将6D位姿估计建模为基于局部特征的条件流匹配问题
- 在BOP五个数据集上平均召回率提升4.5个百分点
- 融合外观语义特征和RANSAC，适合对称或低纹理物体
现有实例级6D位姿估计方法通常依赖神经网络直接回归SE(3)中的位姿，或通过局部特征匹配间接估计。前者难以处理物体对称性，后者在缺乏显著局部特征时失效。为此，我们提出一种新的6D位姿估计形式化方法：将问题转化为ℝ³中的条件流匹配。我们引入Flose，一种生成式方法，通过以局部特征为条件的去噪过程推断物体位姿。与以往仅依赖几何引导的条件流匹配不同，Flose整合了基于外观的语义特征，以缓解由对称性引起的歧义。我们还引入基于RANSAC的注册方法处理异常值。我们在成熟的BOP基准的五个数据集上验证了Flose，其平均召回率相比之前方法提升了+4.5个百分点。
原文摘要 · Abstract (English)
Existing methods for instance-level 6D pose estimation typically rely on neural networks that either directly regress the pose in $\mathrm{SE}(3)$ or estimate it indirectly via local feature matching. The former struggle with object symmetries, while the latter fail in the absence of distinctive local features. To overcome these limitations, we propose a novel formulation of 6D pose estimation as a conditional flow matching problem in $\mathbb{R}^3$. We introduce Flose, a generative method that infers object poses via a denoising process conditioned on local features. While prior approaches based on conditional flow matching perform denoising solely based on geometric guidance, Flose integrates appearance-based semantic features to mitigate ambiguities caused by object symmetries. We further incorporate RANSAC-based registration to handle outliers. We validate Flose on five datasets from the established BOP benchmark. Flose outperforms prior methods with an average improvement of +4.5 Average Recall. Project Website : https://tev-fbk.github.io/Flose/
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。