Papers with Code 아카이브
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/MaXvanHeLL/LSD-SLAM
공식 코드https://github.com/MaaniGhaffari/cvo-rgbd
공식 코드https://github.com/MajidMoghadam2006/deepcars-reinforcement-learning
공식 코드https://github.com/Majiker/STAR
공식 코드Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
https://github.com/ManipulativeConsultant/AutonomousDrivingCookbook
공식 코드Adaptive Symmetric Reward Noising for Reinforcement Learning
https://github.com/Manojbhat09/Trajformer
공식 코드Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving
https://github.com/Marchetz/MANTRA-CVPR20
공식 코드MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction
https://github.com/MarvinTeichmann/MultiNet
공식 코드MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/Mazin-Hnewa/MS-DAYOLO
공식 코드Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection
https://github.com/Megvii-BaseDetection/LLA
공식 코드LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
https://github.com/Megvii-BaseDetection/YOLOX
공식 코드https://github.com/MeixinZhu/Velocity_control
공식 코드https://github.com/MetaSLAM/GPRS
공식 코드General Place Recognition Survey: Towards the Real-world Autonomy Age
https://github.com/Microsoft/AirSim
공식 코드AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/MikeS96/3d_obj_detection
공식 코드High-level camera-LiDAR fusion for 3D object detection with machine learning
https://github.com/MoonBlvd/tad-IROS2019
공식 코드Unsupervised Traffic Accident Detection in First-Person Videos
https://github.com/MoyGcc/hpcwild
공식 코드https://github.com/MrD1360/deep_segmentation_vineyards_navigation
공식 코드Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows
https://github.com/Mrmoore98/VectorMapNet_code
공식 코드https://github.com/MubarizZaffar/VPR-Bench
공식 코드https://github.com/MurpheyLab/CPL-SLAM
공식 코드https://github.com/MushroomHunting/RSS2020-online-domain-adaptation-pot
공식 코드https://github.com/NJUVISION/rho-vision
공식 코드https://github.com/NVIDIA-Jetson/redtail
공식 코드https://github.com/NVlabs/Bi3D
공식 코드https://github.com/NVlabs/BiTTC
공식 코드https://github.com/NVlabs/CTG
공식 코드Guided Conditional Diffusion for Controllable Traffic Simulation
https://github.com/NVlabs/FocalFormer3D
공식 코드FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
https://github.com/Natlem/M-HTCN
공식 코드Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer
https://github.com/NeurAI-Lab/G2S
공식 코드Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation
https://github.com/NeuralSec/Daedalus-attack
공식 코드Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
https://github.com/Nightmare-n/UniPAD
공식 코드UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
https://github.com/NingMingHao/Yolov8TensorrtRos
공식 코드An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion
https://github.com/NomiMalik0207/PedSynth-and-PedGNN-for-Pedestrian-Intention-Prediction
공식 코드https://github.com/OATML/oatomobile
공식 코드Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.