SpaCeFormer delivers 11.1 zero-shot mAP on ScanNet200 (2.8x prior proposal-free best) and runs 2-3 orders of magnitude faster than multi-stage 2D+3D pipelines by using spatial window attention and Morton-curve serialization to predict instance masks from learned queries.
arXiv preprint arXiv:2507.23134 (2025) 9, 12, 13
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CAMFusion fuses multiview 2D vision-language embeddings via cross-attention and multiview consistency self-supervision to produce better 3D semantic and instance representations, outperforming averaging and reaching SOTA on benchmarks including zero-shot out-of-domain cases.
MV3DIS uses 3D-guided mask matching and depth consistency to produce more consistent multi-view 2D masks that refine into accurate zero-shot 3D instances.
citing papers explorer
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SpaCeFormer: Fast Proposal-Free Open-Vocabulary 3D Instance Segmentation
SpaCeFormer delivers 11.1 zero-shot mAP on ScanNet200 (2.8x prior proposal-free best) and runs 2-3 orders of magnitude faster than multi-stage 2D+3D pipelines by using spatial window attention and Morton-curve serialization to predict instance masks from learned queries.
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Cross-Attentive Multiview Fusion of Vision-Language Embeddings
CAMFusion fuses multiview 2D vision-language embeddings via cross-attention and multiview consistency self-supervision to produce better 3D semantic and instance representations, outperforming averaging and reaching SOTA on benchmarks including zero-shot out-of-domain cases.
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MV3DIS: Multi-View Mask Matching via 3D Guides for Zero-Shot 3D Instance Segmentation
MV3DIS uses 3D-guided mask matching and depth consistency to produce more consistent multi-view 2D masks that refine into accurate zero-shot 3D instances.