Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/bertrandlalo/deep_dog_breeds
https://github.com/bethgelab/InDomainGeneralizationBenchmark
https://github.com/bfortuner/pytorch_tiramisu
https://github.com/bharatsush/object_detection
https://github.com/bhaskar-gaur/DenseNet-Keras
https://github.com/billpsomas/Lane_Detection_PyTorch
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/bimsarapathiraja/mccl
https://github.com/binhdv92/darknet
https://github.com/binhdv92/darknet_kriyeng
https://github.com/bitsauce/Carla-ppo
https://github.com/blueardour/AdelaiDet
https://github.com/bobby20180331/darknet_pycharm
https://github.com/bobbyhaliwela/AutoRcCar
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/bobbyhaliwela/AutoRcCar-Behavioral-Clonning
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/bolianchen/nuscenes_depth
https://github.com/boris127/vehicle-detection
https://github.com/bosch-ros-pkg/bstld
https://github.com/bostondiditeam/MV3D
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/bouracha/Gen_Motion
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/bouracha/OoDMotion
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/bowensong0312/darknet
https://github.com/bozliu/E2E-Keyword-Spotting
https://github.com/bradenhurl/DeepGTAV-PreSIL
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/GTAVisionExport-DepthExtractor
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/PreSIL-tools
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/TruPercept
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
https://github.com/bradyz/2020_CARLA_challenge
https://github.com/bravegroup/fullysparsefusion
https://github.com/brycexu/MR-Residual-Net
https://github.com/bryonkucharski/Learning-to-Drive-with-Reinforcement-Learning-and-Variational-Autoencoders
https://github.com/bryonkucharski/learning-to-drive-in-a-day-reproduction
https://github.com/brytsknguyen/M-LOAM
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
https://github.com/bsj1216/NNMPC
https://github.com/buaaYYC/faster-rcnn-tensorflow
https://github.com/buptdbj/darknet-windows-linux
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.