https://github.com/TencentARC/ViT-Lens
공식 코드ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
Papers with Code 아카이브
Training-free 3D Point Cloud Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Evaluation on target datasets for 3D Point Cloud Classification without any training
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/asalarpour/Point_GN
공식 코드Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
https://github.com/salesforce/ulip
공식 코드ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
https://github.com/tyhuang0428/CLIP2Point
공식 코드CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
https://github.com/yangyangyang127/pointclip_v2
공식 코드PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
https://github.com/ziyuguo99/calip
공식 코드CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
https://github.com/zrrskywalker/pointclip
공식 코드https://github.com/opengvlab/cafo
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
https://github.com/pku-dair/hetu
https://github.com/zrrskywalker/point-nn
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.