Learning universal representations across different applications domain is an open research problem. In fact, finding universal architecture within the same application but across different types of datasets is still unsolved problem too, especially in applications involving processing 3D point clouds. In this work we experimentally test several state-of-the-art learning-based methods for 3D point cloud registration against the proposed non-learning baseline registration method. The proposed method either outperforms or achieves comparable results w.r.t. learning based methods. In addition, we propose a dataset on which learning based methods have a hard time to generalize. Our proposed method and dataset, along with the provided experiments, can be used in further research in studying effective solutions for universal representations. Our source code is available at: github.com/DavidBoja/greedy-grid-search.

我们展示了自我监督学习的使用来探索和利用大型未标记的数据集。从最新数据释放的暗能谱仪器（DESI）传统成像调查中侧重于4200万个Galaxy图像，我们首先培养一个自我监督模型来蒸馏到对称，不确定性和每个噪声的强大稳健图片。然后，我们使用表示来构建和公开发布交互式语义相似性搜索工具。我们展示了我们的工具如何用于迅速发现罕见的物体，仅给出一个例子，提高人群采购活动的速度，并构建和改进监督应用程序的培训集。虽然我们专注于Sky调查的图像，但该技术很简单适用于任何维度的任何科学数据集。可以在https://github.com/georgestein/galaxy_search找到相似性搜索Web应用程序

我们采用自我监督的代表性学习来从深色能源仪器遗产成像调查的数据释放9中从7600万个星系图像中提取信息9.针对新的强力引力镜头候选者的识别，我们首先创建了快速的相似性搜索工具，以发现新的搜索工具强镜仅给出一个单个标记的示例。然后，我们展示如何在自我监督的表示上训练简单的线性分类器，仅需几分钟即可在CPU上进行几分钟，可以自动以极高的效率对强镜进行分类。我们提出了1192个新的强镜候选者，我们通过简短的视觉标识活动确定，并释放一种基于Web的相似性搜索工具和顶级网络预测，以促进众包快速发现额外的强力镜头和其他稀有物体：HTTPS：https：//github.com/georgestein/ssl-legacysurvey。