https://github.com/Healthcare-Robotics/spectrovision
공식 코드Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
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Material Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ZFTurbo/DrivenData-Open-AI-Caribbean-Challenge-2nd-place-solution
공식 코드https://github.com/aniketdashpute/tsf
공식 코드Thermal Spread Functions (TSF): Physics-guided Material Classification
https://github.com/apple/ml-dms-dataset
공식 코드A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
https://github.com/chenyunwu/clip_texture
공식 코드https://github.com/deepneuroscience/DeepThermalImaging
공식 코드https://github.com/fwestling/SimTreeLS
공식 코드SimTreeLS: Simulating aerial and terrestrial laser scans of trees
https://github.com/herolab-uga/matsee
공식 코드https://github.com/ligerfotis/mvitac
공식 코드Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training
https://github.com/prakashchhipa/depth-contrast-self-supervised-method
공식 코드Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
https://github.com/Healthcare-Robotics/smm50
https://github.com/IBM/ZOSVRG-BlackBox-Adv
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
https://github.com/junweizheng93/materobot
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
https://github.com/kebasaa/SCIO-read
https://github.com/sagieppel/MatSim-Dataset-Generator-Scripts-And-Neural-net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.