LangTail uses entity-level semantic priors from language models aligned via contrastive learning in a hierarchical clustering setup to resolve long-tail ambiguity, yielding +13.5, +12.9, and +8.9 mIoU gains on ScanNet-v2, S3DIS, and nuScenes.
Foundational models for 3d point clouds: A survey and outlook
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Feature-space sampling in GCNNs preserves 3D classification accuracy with coarse discretization, enabling precomputation and faster training of equivariant models.
C-GenReg achieves training-free 3D point cloud registration by generating multi-view-consistent images from geometry, extracting VFM correspondences, and probabilistically fusing them with raw geometric matches for zero-shot performance on indoor and outdoor benchmarks.
citing papers explorer
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Resolving Long-Tail Ambiguity in Unsupervised 3D Point Cloud Segmentation with Language Priors
LangTail uses entity-level semantic priors from language models aligned via contrastive learning in a hierarchical clustering setup to resolve long-tail ambiguity, yielding +13.5, +12.9, and +8.9 mIoU gains on ScanNet-v2, S3DIS, and nuScenes.
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Discretizing Group-Convolutional Neural Networks for 3D Geometry in Feature Space
Feature-space sampling in GCNNs preserves 3D classification accuracy with coarse discretization, enabling precomputation and faster training of equivariant models.
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C-GenReg: Training-Free 3D Point Cloud Registration by Multi-View-Consistent Geometry-to-Image Generation with Probabilistic Modalities Fusion
C-GenReg achieves training-free 3D point cloud registration by generating multi-view-consistent images from geometry, extracting VFM correspondences, and probabilistically fusing them with raw geometric matches for zero-shot performance on indoor and outdoor benchmarks.