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Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ethnhe/FS6D-PyTorch
공식 코드https://github.com/ethz-asl/neusurfemb
공식 코드https://github.com/ethz-asl/object_keypoints
공식 코드Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
https://github.com/ethz-asl/tsdf-plusplus
공식 코드TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction
https://github.com/event-ahu/openevdet
공식 코드https://github.com/evinpinar/supergb-d
공식 코드SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments
https://github.com/exiawsh/streampetr
공식 코드Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
https://github.com/expertailab/textual-entailment-for-effective-triple-validation-in-object-prediction
공식 코드Textual Entailment for Effective Triple Validation in Object Prediction
https://github.com/explainableml/isco
공식 코드Iterative Superquadric Recomposition of 3D Objects from Multiple Views
https://github.com/fabiankueppers/calibration-framework
공식 코드https://github.com/fabienbaradel/object_level_visual_reasoning
공식 코드https://github.com/fabiencode/lg3d
공식 코드https://github.com/facebookresearch/ContactPose
공식 코드ContactPose: A Dataset of Grasps with Object Contact and Hand Pose
https://github.com/facebookresearch/DepthContrast
공식 코드Self-Supervised Pretraining of 3D Features on any Point-Cloud
https://github.com/facebookresearch/Needles-in-Haystacks
공식 코드Needles in Haystacks: On Classifying Tiny Objects in Large Images
https://github.com/facebookresearch/SlowFast
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/adaptive_teacher
공식 코드https://github.com/facebookresearch/assemblyhands-toolkit
공식 코드Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
https://github.com/facebookresearch/c3dm
공식 코드https://github.com/facebookresearch/co3d
공식 코드Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
https://github.com/facebookresearch/d3d-hoi
공식 코드https://github.com/facebookresearch/detectron
공식 코드https://github.com/facebookresearch/detectron2/tree/main/projects/MViTv2
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet
공식 코드Exploring Plain Vision Transformer Backbones for Object Detection
https://github.com/facebookresearch/detectron2/tree/master/projects/PointSup
공식 코드https://github.com/facebookresearch/detectron2/tree/master/projects/TensorMask
공식 코드https://github.com/facebookresearch/detr
공식 코드https://github.com/facebookresearch/egolifter
공식 코드EgoLifter: Open-world 3D Segmentation for Egocentric Perception
https://github.com/facebookresearch/egoobjects
공식 코드EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
https://github.com/facebookresearch/habitat-lab
공식 코드https://github.com/facebookresearch/maskrcnn-benchmark
공식 코드Is Heuristic Sampling Necessary in Training Deep Object Detectors?
https://github.com/facebookresearch/multigrain
공식 코드MultiGrain: a unified image embedding for classes and instances
https://github.com/facebookresearch/mvit
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/omni3d
공식 코드Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
https://github.com/facebookresearch/paco
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.