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3D Shape Representation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries
Self-supervised Graph Neural Network for Mechanical CAD Retrieval
Generative AI-based Prompt Evolution Engineering Design Optimization With Vision-Language Model
Unsupervised Occupancy Learning from Sparse Point Cloud
Unsupervised Template-assisted Point Cloud Shape Correspondence Network
TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding
TIFu: Tri-directional Implicit Function for High-Fidelity 3D Character Reconstruction
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Mosaic-SDF for 3D Generative Models
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation
Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features
EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency
On the Localization of Ultrasound Image Slices within Point Distribution Models
Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map
Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification
3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability
Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane
3D VR Sketch Guided 3D Shape Prototyping and Exploration
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
CN-DHF: Compact Neural Double Height-Field Representations of 3D Shapes
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence
Multi-View Photometric Stereo Revisited
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
Taylor3DNet: Fast 3D Shape Inference With Landmark Points Based Taylor Series
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