Papers with Code 아카이브
Point Cloud Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bgucompsci/multigridinchannelscnns
공식 코드https://github.com/calad0i/HGQ
공식 코드Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
https://github.com/calad0i/HGQ-demos
공식 코드Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
https://github.com/cgye96/a_closer_look_at_3dfsl
공식 코드https://github.com/chenfengxu714/image2point
공식 코드Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
https://github.com/cszyzhang/RISurConv
공식 코드https://github.com/cuevhv/gelatto
공식 코드https://github.com/cvlab-unibo/refrec
공식 코드RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
https://github.com/cyysc1998/3D-Pretraining
공식 코드Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes
https://github.com/diegovalsesia/hycore
공식 코드Rethinking the compositionality of point clouds through regularization in the hyperbolic space
https://github.com/dogyoonlee/RSMix-official
공식 코드Regularization Strategy for Point Cloud via Rigidly Mixed Sample
https://github.com/ds0529/RTN
공식 코드https://github.com/explain3d/lime-3d
공식 코드Surrogate Model-Based Explainability Methods for Point Cloud NNs
https://github.com/explain3d/pointcloudam
공식 코드https://github.com/fanglaosi/pointmls
공식 코드ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification
https://github.com/farakiko/xai4hep
공식 코드Do graph neural networks learn traditional jet substructure?
https://github.com/fdreyer/LundNet
공식 코드https://github.com/fei960922/GPointNet
공식 코드https://github.com/fukexue/mcp-bert
공식 코드https://github.com/giorgiocerro/jetlov
공식 코드JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables
https://github.com/gorilla-lab-scut/qs3
공식 코드Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
https://github.com/guochengqian/assanet
공식 코드ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
https://github.com/guochengqian/pix4point
공식 코드Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding
https://github.com/haibo-qiu/cdformer
공식 코드Collect-and-Distribute Transformer for 3D Point Cloud Analysis
https://github.com/hansen7/OcCo
공식 코드Unsupervised Point Cloud Pre-Training via Occlusion Completion
https://github.com/hftsoi/symbolnet
공식 코드SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
https://github.com/hkust-vgd/scanobjectnn
공식 코드https://github.com/hlei-ziyan/SPH3D-GCN
공식 코드Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
https://github.com/hqucms/weaver-core
공식 코드https://github.com/hrzhou2/adaptconv-master
공식 코드https://github.com/ignf/myria3d
공식 코드https://github.com/ignf/pacasam
공식 코드https://github.com/j-geuter/ddeqs
공식 코드DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
https://github.com/jaronthu/dumlp-pin
공식 코드DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature Extraction
https://github.com/jeffwillette/umbc
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.