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

We present a new method for the unsupervised detection of geometric anomalies
in high-resolution 3D point clouds. In particular, we propose an adaptation of
the established student-teacher anomaly detection framework to three
dimensions. A student network is trained to match the output of a pretrained
teacher network on anomaly-free point clouds. When applied to test data,
regression errors between the teacher and the student allow reliable
localization of anomalous structures. To construct an expressive teacher
network that extracts dense local geometric descriptors, we introduce a novel
self-supervised pretraining strategy. The teacher is trained by reconstructing
local receptive fields and does not require annotations. Extensive experiments
on the comprehensive MVTec 3D Anomaly Detection dataset highlight the
effectiveness of our approach, which outperforms the next-best method by a
large margin. Ablation studies show that our approach meets the requirements of
practical applications regarding performance, runtime, and memory consumption.

提出了一种用于高分辨率三维点云的无监督检测几何异常的新方法，该方法将学生 - 教师异常检测框架适应到三维领域，并利用一种自监督预训练策略构建具有表达能力的教师网络，实现局部几何描述符的提取，实验结果表明该方法在性能、运行时间和内存消耗等方面都具有较好的应用前景。