Object 6D pose estimation methods can achieve high accuracy when trained and
tested on the same objects. However, estimating the pose of objects that are
absent at training time is still a challenge. In this work, we advance the
state-of-the-art in zero-shot object 6D pose estimation by proposing the first
method that fuses the contribution of pre-trained geometric and vision
foundation models. Unlike state-of-the-art approaches that train their pipeline
on data specifically crafted for the 6D pose estimation task, our method does
not require task-specific finetuning. Instead, our method, which we name PoMZ,
combines geometric descriptors learned from point cloud data with visual
features learned from large-scale web images to produce distinctive 3D
point-level descriptors. By applying an off-the-shelf registration algorithm,
like RANSAC, PoMZ outperforms all state-of-the-art zero-shot object 6D pose
estimation approaches. We extensively evaluate PoMZ across the seven core
datasets of the BOP Benchmark, encompassing over a hundred objects and 20
thousand images captured in diverse scenarios. PoMZ ranks first in the BOP
Benchmark under the category Task 4: 6D localization of unseen objects. We will
release the source code publicly.

提出了一种名为 PoMZ 的新方法，通过融合预训练的几何和图像模型，实现了零样本物体 6D 位姿估计的最新进展，该方法无需任务特定的微调，在 BOP 基准测试中在无法见到的物体的 6D 定位领域取得了第一名。