We propose FoundPose, a method for 6D pose estimation of unseen rigid objects
from a single RGB image. The method assumes that 3D models of the objects are
available but does not require any object-specific training. This is achieved
by building upon DINOv2, a recent vision foundation model with impressive
generalization capabilities. An online pose estimation stage is supported by a
minimal object representation that is built during a short onboarding stage
from DINOv2 patch features extracted from rendered object templates. Given a
query image with an object segmentation mask, FoundPose first rapidly retrieves
a handful of similarly looking templates by a DINOv2-based bag-of-words
approach. Pose hypotheses are then generated from 2D-3D correspondences
established by matching DINOv2 patch features between the query image and a
retrieved template, and finally optimized by featuremetric refinement. The
method can handle diverse objects, including challenging ones with symmetries
and without any texture, and noticeably outperforms existing RGB methods for
coarse pose estimation in both accuracy and speed on the standard BOP
benchmark. With the featuremetric and additional MegaPose refinement, which are
demonstrated complementary, the method outperforms all RGB competitors. Source
code is at: evinpinar.github.io/foundpose.

利用 FoundPose 方法可以从单个 RGB 图像中准确估计看不见的刚性物体的 6D 姿态，方法基于 DINOv2 模型实现泛化能力强，通过 DINOv2 基于词袋模型快速检索相似模板，从而建立 2D-3D 对应，最后通过特征度量优化姿态。该方法可以应对各种物体，包括具有对称性和无纹理的复杂物体，在标准 BOP 基准测试中在粗略姿态估计的准确性和速度方面明显优于现有的 RGB 方法，而在特征度量和额外的 MegaPose 优化的情况下，该方法胜过所有的 RGB 竞争者。