← 研踪 PaperTrailarXiv:2609.00775 · v2
VOIM: Training-Free Open-Vocabulary 3D Instance Mapping for RGB-D and Monocular SLAM
首次提交 2026-09-01 14:07
版本更新 2026-11-09 06:43
雷达观测 2026-11-10 13:19
摘要
Robots that act on everyday language requests need maps that record what each object is and where it is. Existing open-vocabulary 3D mapping systems usually require a depth sensor and commit to each object the first time they see it, when the evidence is weakest. We present VOIM, a training-free system that builds open-vocabulary 3D instance maps from RGB-D or from monocular RGB video alone. VOIM accumulates soft label evidence from frozen, off-the-shelf perception models in every voxel across views, and decides labels and object instances only after that evidence has built up. On ScanNet++, VOIM achieves the highest present-class mIoU among the compared methods, 10 points above the strongest baseline. From monocular video alone, VOIM also exceeds HOV-SG and ConceptGraphs run with depth and ground-truth poses. We also conduct real-robot navigation experiments on a quadruped to show that VOIM maps are usable in the real world.
作者备注：8 pages, 4 figures, 5 tables
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v2 · 2026-11-09 06:43 · 最新观测版本
VOIM: Training-Free Open-Vocabulary 3D Instance Mapping for RGB-D and Monocular SLAM
Robots that act on everyday language requests need maps that record what each object is and where it is. Existing open-vocabulary 3D mapping systems usually require a depth sensor and commit to each object the first time they see it, when the evidence is weakest. We present VOIM, a training-free system that builds open-vocabulary 3D instance maps from RGB-D or from monocular RGB video alone. VOIM accumulates soft label evidence from frozen, off-the-shelf perception models in every voxel across views, and decides labels and object instances only after that evidence has built up. On ScanNet++, VOIM achieves the highest present-class mIoU among the compared methods, 10 points above the strongest baseline. From monocular video alone, VOIM also exceeds HOV-SG and ConceptGraphs run with depth and ground-truth poses. We also conduct real-robot navigation experiments on a quadruped to show that VOIM maps are usable in the real world.
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