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。

6D 物体位姿估计结合零样本学习

Object 6D pose estimation meets zero-shot learning

This document summarizes the 4th International Workshop on Recovering 6D
Object Pose which was organized in conjunction with ECCV 2018 in Munich. The
workshop featured four invited talks, oral and poster presentations of accepted
workshop papers, and an introduction of the BOP benchmark for 6D object pose
estimation. The workshop was attended by 100+ people working on relevant topics
in both academia and industry who shared up-to-date advances and discussed open
problems.

总结了 2018 年 ECCV 在慕尼黑举办的第 4 届 6D 物体姿态恢复的国际研讨会，包括四个特邀演讲，接受的论文的口头报告和海报展示，以及介绍 6D 物体姿态估计的 BOP 基准。研讨会吸引了 100 多位从事相关学术和产业领域的人士，分享最新进展并讨论未解决的问题。