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.
Dynamic graph cnn for learning on point clouds,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
Empowered t-FCW graph representation provides a unified non-parametric and interpretable method for point cloud analysis with high efficiency on ModelNet40 classification.
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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Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
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A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation
Empowered t-FCW graph representation provides a unified non-parametric and interpretable method for point cloud analysis with high efficiency on ModelNet40 classification.