MeshTailor is a mesh-native generative model that uses ChainingSeams serialization and a dual-stream transformer with pointer layers to trace coherent seams vertex-by-vertex on 3D surfaces.
Sampart3d: Segment any part in 3d objects
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
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A framework for robust 3D segmentation in editable Gaussian Splatting that combines SAM-HQ masks with prior-guided multiview-consistent label assignment to 3D Gaussians.
Robots discover causal tool features through VLM suggestions and physics-based counterfactual perturbations in simulation, then transfer manipulation skills via conditioned keypoint matching.
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.
STEP-Parts produces tessellation-robust geometric part labels from STEP B-Reps by deterministic merging of same-primitive faces, enabling consistent supervision on 180k+ models.
S2AM3D combines multi-view 2D priors with 3D contrastive learning and a scale-aware decoder to deliver consistent, granularity-controllable part segmentation on point clouds, supported by a new dataset exceeding 100k samples.
citing papers explorer
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MeshTailor: Cutting Seams via Generative Mesh Traversal
MeshTailor is a mesh-native generative model that uses ChainingSeams serialization and a dual-stream transformer with pointer layers to trace coherent seams vertex-by-vertex on 3D surfaces.
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Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting
A framework for robust 3D segmentation in editable Gaussian Splatting that combines SAM-HQ masks with prior-guided multiview-consistent label assignment to 3D Gaussians.
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Creative Robot Tool Use by Counterfactual Reasoning
Robots discover causal tool features through VLM suggestions and physics-based counterfactual perturbations in simulation, then transfer manipulation skills via conditioned keypoint matching.
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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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From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation
The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.
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STEP-Parts: Geometric Partitioning of Boundary Representations for Large-Scale CAD Processing
STEP-Parts produces tessellation-robust geometric part labels from STEP B-Reps by deterministic merging of same-primitive faces, enabling consistent supervision on 180k+ models.
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S2AM3D: Scale-controllable Part Segmentation of 3D Point Clouds
S2AM3D combines multi-view 2D priors with 3D contrastive learning and a scale-aware decoder to deliver consistent, granularity-controllable part segmentation on point clouds, supported by a new dataset exceeding 100k samples.