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/wuyangzhang/maskrcnn
https://github.com/wwooo/tensorflow_complex_yolo
https://github.com/xcmyz/Face-Binary-Classification
https://github.com/xcmyz/FaceDetection
https://github.com/xcmyz/Face_Binary_Classfication
https://github.com/xdliangdi/AirsimUnity
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/xdliangdi/airSim
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/xhlulu/arxiv-assistant
https://github.com/xhwind/hm-vit
HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer
https://github.com/xiamenwcy/extended-caffe
https://github.com/xiaoaoran/SynLiDAR
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
https://github.com/xiaofeng-han-res/mf-rv
Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision
https://github.com/xiaogangLi/tensorflow-Darknet53-YOLOv3
https://github.com/xiaogangpeng/socialtgcn
Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting
https://github.com/xiaoxd16/Palm
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xiayuyang/AUTO
Parameterized Decision-making with Multi-modal Perception for Autonomous Driving
https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P6-Kidnapped-Vehicle
https://github.com/xiexiexiaoxiexie/Udacity-self-driving-car-engineer-P7-Highway-Driving
Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation
https://github.com/xifen523/cod
EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving
https://github.com/xincoder/GRIP_Trajectory
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xinpingwang/tf-faster-rcnn
https://github.com/xinshasha/YoloV3
https://github.com/xinwei666/mmgenerativeir
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/tutorials/tree/main/yolov3
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xiya888/lane_detect_convert
https://github.com/xiyie/yolox
https://github.com/xk-huang/yet-another-vectornet
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xuannianz/keras-GaussianYOLOv3
https://github.com/xuannianz/keras-fcos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.