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

원문 초록

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.