English(EN) VOIM: Training-Free Open-Vocabulary 3D Instance Mapping for RGB-D and Monocular SLAM

VOIM系统无需训练即可生成3D实例地图

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员推出了一种新颖的、无需训练的VOIM系统，可从RGB-D或单目RGB数据创建3D实例地图。与早期承诺标签的现有在线系统不同，VOIM将这些决策推迟到积累了足够证据后，从而提高了准确性。在ScanNet++上的评估中，VOIM在平均交并比（mIoU）方面显著优于领先的在线RGB-D系统OVO-SLAM。该系统在适应单目RGB输入时也显示出有效性，在Replica数据集上与基线相匹配。 AI

影响这项研究引入了一种新颖的3D映射方法，有望提高机器人和增强现实应用中的场景理解和物体识别能力。

排序理由这是一篇详细介绍3D实例映射新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Sangmin Song, Sarath Kodagoda, Marc G. Carmichael, Karthick Thiyagarajan, Amal Gunatilake, Kelly Prentice, Jodi Martin · 2026-09-02 04:00

VOIM：RGB-D和单目SLAM的免训练开放词汇3D实例映射

arXiv:2609.00775v1 Announce Type: cross Abstract: We present Voxel-Grounded Online Instance Manager (VOIM), a training-free voxel-grounded instance manager that builds open-vocabulary 3D instance maps from RGB-D or from monocular RGB alone, a regime no prior training-free system …

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VOIM：RGB-D和单目SLAM的免训练开放词汇3D实例映射

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