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/Lornatang/PyTorch-yolov3
https://github.com/Lornatang/ResNet-PyTorch
https://github.com/Lornatang/YOLOv3-PyTorch
https://github.com/LuigiRussoDev/Covid19Detection
https://github.com/LuigiRussoDev/ResNets
https://github.com/Lxrd-AJ/Advanced_ML
A Review on Deep Learning Techniques Applied to Semantic Segmentation
https://github.com/M155K4R4/autopilot
https://github.com/M2219/pyOrbSLAM
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
https://github.com/MCG-NJU/VideoChat-Online
https://github.com/MIC-DKFZ/DetectionAndRegression
https://github.com/MIC-DKFZ/RegRCNN
https://github.com/MINED30/Face_Mask_Detection_YOLO
https://github.com/MIT-SPARK/Kimera-Evaluation
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/MIT-SPARK/Kimera-RPGO
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-Semantics
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-VIO
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/MIT-SPARK/Kimera-VIO-Evaluation
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/MIT-SPARK/Kimera-VIO-ROS
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MK-tech20/CGA2M_plus
Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
https://github.com/MRZWD/yolov3_caltech
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet
https://github.com/MS-Mind/MS-Code-06/tree/main/OctSqueeze
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
https://github.com/MS-Mind/MS-Code-08/tree/main/MGN
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
https://github.com/MSJawad/yolov3
https://github.com/Madhusakth/Adaptive-Radar-Acquisition
https://github.com/MalayAgarwal-Lee/SteeringWheelAnglePredictor
https://github.com/MalongTech/research-fad
https://github.com/Manjuphoenix/IndraEye
https://github.com/Manojbhat09/DATF-Trajformer
Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving
https://github.com/MapItAnywhere/MapItAnywhere
Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data
https://github.com/MariefW/6.-trainingYOLO
https://github.com/MarkHershey/arxiv-dl
https://github.com/MarkRoy8888/CarND-Project-4-Behavioral-Cloning
https://github.com/MartinHahner/FoggySynscapes
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
https://github.com/MartinHahner88/FoggySynscapes
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.