← 研踪 PaperTrailCVF · CVPR 2026:cvf:fbac7ca69499577809facd5327bb1101 · 记录 #1
SAMosaic3D: Modular Scene Assembly for Real-Time 3D Segment Anything
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:41
摘要
Online 3D instance segmentation is a critical capability for embodied agents navigating in dynamic environments. However, a fundamental challenge remains in adapting powerful 2D foundation models, like SAM, to 3D online segmentation. Naively lifting SAM's 2D masks to 3D results in severe spatial fragmentation, where a single object is shattered into multiple disconnected parts, especially under occlusion. Subsequent attempts to link these fragments over time via conventional 3D IoU-based tracking prove highly fragile: they struggle to handle occlusions or topological changes, ultimately causing catastrophic identity drift. Departing from such post-processing approaches, we reframe online segmentation as a learnable composition problem. We introduce SAMosaic3D, a differentiable framework that treats SAM-derived masks as "mosaic tiles" and learns to assemble them into temporally consistent 3D instances. SAMosaic3D comprises two key components: Fragment-to-Instance Adaptive Assembly that aggregates fragments through soft-gated attention, and Instance-to-Scene Online Merging that employs cascaded semantic-geometric matching to preserve object identities through learned short-term association and geometric verification for long-term re-identification. Evaluations on ScanNet, ScanNet200, SceneNN and 3RScan datasets demonstrate state-of-the-art performance and zero-shot cross-dataset generalization. Extensive ablation studies validate the effectiveness of the designed modules.
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记录 #1 · 2026 · 最新观测版本
SAMosaic3D: Modular Scene Assembly for Real-Time 3D Segment Anything
Online 3D instance segmentation is a critical capability for embodied agents navigating in dynamic environments. However, a fundamental challenge remains in adapting powerful 2D foundation models, like SAM, to 3D online segmentation. Naively lifting SAM's 2D masks to 3D results in severe spatial fragmentation, where a single object is shattered into multiple disconnected parts, especially under occlusion. Subsequent attempts to link these fragments over time via conventional 3D IoU-based tracking prove highly fragile: they struggle to handle occlusions or topological changes, ultimately causing catastrophic identity drift. Departing from such post-processing approaches, we reframe online segmentation as a learnable composition problem. We introduce SAMosaic3D, a differentiable framework that treats SAM-derived masks as "mosaic tiles" and learns to assemble them into temporally consistent 3D instances. SAMosaic3D comprises two key components: Fragment-to-Instance Adaptive Assembly that aggregates fragments through soft-gated attention, and Instance-to-Scene Online Merging that employs cascaded semantic-geometric matching to preserve object identities through learned short-term association and geometric verification for long-term re-identification. Evaluations on ScanNet, ScanNet200, SceneNN and 3RScan datasets demonstrate state-of-the-art performance and zero-shot cross-dataset generalization. Extensive ablation studies validate the effectiveness of the designed modules.
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