3D 视觉

MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding

MMLANDMARKS: 一种用于地理空间理解的跨视图实例级基准

Oskar Kristoffersen, Alba Reinders Sánchez, Morten Rieger Hannemose, Anders Bjorholm Dahl, Dim P. Papadopoulos

机构 * Technical University of Denmark（丹麦技术大学） ； Pioneer Center for AI（先锋人工智能中心） 纠错

专题命中 空间理解 ：spatial understanding(title,abstract)；分类 cs.CV

AI总结 本文提出MMLandmarks基准，包含四类数据：高分辨率航拍图、地面视图、文本信息和地理坐标，用于跨视图地物检索、定位及文本到图像等任务，揭示多模态数据对地理空间理解的重要性。

Comments Accepted at CVPR 2026

MiMo-Embodied: X-Embodied Foundation Model Technical Report

MiMo-Embodied：X-Embodied基础模型技术报告

Xiaoshuai Hao, Lei Zhou, Zhijian Huang, Zhiwen Hou, Yingbo Tang, Lingfeng Zhang, Guang Li, Zheng Lu, Shuhuai Ren, Xianhui Meng, Yuchen Zhang, Jing Wu, Jinghui Lu, Chenxu Dang, Jiayi Guan, Jianhua Wu, Zhiyi Hou, Hanbing Li, Shumeng Xia, Mingliang Zhou, Yinan Zheng, Zihao Yue, Shuhao Gu, Hao Tian, Yuannan Shen, Jianwei Cui, Wen Zhang, Shaoqing Xu, Bing Wang, Haiyang Sun, Zeyu Zhu, Yuncheng Jiang, Zibin Guo, Chuhong Gong, Chaofan Zhang, Wenbo Ding, Kun Ma, Guang Chen, Rui Cai, Diyun Xiang, Heng Qu, Fuli Luo, Hangjun Ye, Long Chen

机构 * Xiaomi Embodied Intelligence Team（小米具身智能团队） 纠错

专题命中 空间理解 ：spatial understanding(abstract)；分类 cs.CV、cs.RO

AI总结 MiMo-Embodied是首个跨具身基础模型，成功整合并实现自动驾驶和具身AI领域的最先进性能，在17个具身AI基准和12个自动驾驶基准中均取得新纪录，通过多阶段学习、数据构建和CoT/RL微调实现领域间的强正迁移。

Comments Code: https://github.com/XiaomiMiMo/MiMo-Embodied | Model: https://huggingface.co/XiaomiMiMo/MiMo-Embodied-7B