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3D Part Segmentation
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Segmenting 3D object parts
<span style="color:grey; opacity: 0.6">( Image credit: MeshCNN: A Network with an Edge )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
HoloPart: Generative 3D Part Amodal Segmentation
Open-Vocabulary Semantic Part Segmentation of 3D Human
Rethinking Masked Representation Learning for 3D Point Cloud Understanding
3D Part Segmentation via Geometric Aggregation of 2D Visual Features
Find Any Part in 3D
SAMPart3D: Segment Any Part in 3D Objects
Search3D: Hierarchical Open-Vocabulary 3D Segmentation
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning
Part123: Part-aware 3D Reconstruction from a Single-view Image
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Model
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding
MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding
PartSTAD: 2D-to-3D Part Segmentation Task Adaptation
PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation
PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
TomoSAM: a 3D Slicer extension using SAM for tomography segmentation
Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation
APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
Self-positioning Point-based Transformer for Point Cloud Understanding
Attention-based Point Cloud Edge Sampling
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
SegNeRF: 3D Part Segmentation with Neural Radiance Fields
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
SageMix: Saliency-Guided Mixup for Point Clouds
Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.