Papers with Code 아카이브
Grasp Generation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Johann-Huber/qd_grasp
공식 코드Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
https://github.com/NVlabs/contact_graspnet
공식 코드Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
https://github.com/StanfordMSL/Splat-MOVER
공식 코드Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting
https://github.com/THU-VCLab/HGGD
공식 코드Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
https://github.com/florasion/d-vqvae
공식 코드Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation
https://github.com/hzwang96/pegg-net
공식 코드PEGG-Net: Pixel-Wise Efficient Grasp Generation in Complex Scenes
https://github.com/ivalab/kgn
공식 코드Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input
https://github.com/junggy/housecat6d
공식 코드https://github.com/korrawe/grasping_field
공식 코드Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/korrawe/grasping_field_demo
공식 코드Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/lixiny/oakink
공식 코드OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction
https://github.com/nickgkan/orange
공식 코드Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation
https://github.com/otaheri/GRAB
공식 코드https://github.com/scenediffuser/Scene-Diffuser
공식 코드Diffusion-based Generation, Optimization, and Planning in 3D Scenes
https://github.com/stevenlsw/contactgen
공식 코드ContactGen: Generative Contact Modeling for Grasp Generation
https://github.com/wenbowen123/catgrasp
공식 코드CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
https://github.com/NVlabs/6dof-graspnet
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
https://github.com/grgward108/PosePretrain
A Unified Transformer-Based Framework with Pretraining For Whole Body Grasping Motion Generation
https://github.com/hsp-iit/byogg
Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping
https://github.com/jsll/pytorch_6dof-graspnet
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
https://github.com/otaheri/GrabNet
https://github.com/zerchen/AlignSDF
Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/zerchen/gSDF
Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/zhouxian/FluidLab
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.