Autonomous Vehicles
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Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.
<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/iyyun/Part-CNN
https://github.com/j3soon/arxiv-utils
https://github.com/jabb0/fastflow3d
https://github.com/jacobkrantz/VLN-CE
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/jaganadhg/tf2x_eval
https://github.com/jahutwb/DL_dosimetry
https://github.com/jameswang287/Car-Detection
https://github.com/jaryp/confidencebranchnetwok
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/jayin301/2021AutonomousACCar
https://github.com/jbernoz/deeppolyp
https://github.com/jchengai/planTF
https://github.com/jcheunglin/Full-Resolution-Residual-Networks-with-PyTorch
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
https://github.com/jday96314/Kuzushiji
https://github.com/jeapostrophe/monaco
https://github.com/jeffwang987/drivedreamer
DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
https://github.com/jellywangjie/yolov3
https://github.com/jeongjin0/detector-cleanse
https://github.com/jesse01/paddle-yolox
https://github.com/jessychen1016/yolov3Onpytorch
https://github.com/jfc4050/detect-to-track
https://github.com/jfpettit/jfpettit.github.io
https://github.com/jgaleav/AirSim
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/jhkim-spa/FVNet_v2
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/jhonve/implicitpcda
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jianmingwuhasco/yolov3
https://github.com/jiaxue1993/pytorch-material-classification
Differential Viewpoints for Ground Terrain Material Recognition
https://github.com/jih189/yolo3
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jingweiz/pytorch-dnc
https://github.com/jinlong17/CoMamba
CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
https://github.com/jinlong17/advgps
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
https://github.com/jinsheng124/yolox
https://github.com/jiweibo/imagenet
https://github.com/jjdiep/Multi-Object-Tracking
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.