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Autonomous Vehicles

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하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

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/abcxs/maskrcnn-contest

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/abhi1kumar/M3D-RPN

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/abhiCoder123/Self-Driving-Car-Tensorflow

End to End Learning for Self-Driving Cars

https://github.com/abhigoku10/CenterPoint_PC

Center-based 3D Object Detection and Tracking

https://github.com/abhileshborode/Behavorial-Clonng-Self-driving-cars

End to End Learning for Self-Driving Cars

https://github.com/abuchin/resnet_sign

Deep Residual Learning for Image Recognition

https://github.com/aby2s/sharpmask

Deep Residual Learning for Image Recognition

https://github.com/achen353/Taiwanese-Traffic-Object-Detection

YOLOv3: An Incremental Improvement

https://github.com/achiyaj/vqa-sandbox

Feature Pyramid Networks for Object Detection

https://github.com/acoMCMXCVI/End-to-end-Conditional-Imitation-Driving

End to End Learning for Self-Driving Cars

https://github.com/adairliulei/yolov3

YOLOv3: An Incremental Improvement

https://github.com/adam-dziedzic/winograd

Fast Algorithms for Convolutional Neural Networks

https://github.com/adam123wu/edge-yolo

YOLOv3: An Incremental Improvement

https://github.com/adamtwig/segpred

Recurrent Flow-Guided Semantic Forecasting

https://github.com/addisonklinke/pytorch-architectures/blob/377548954f13d32e45e5641396e3839a9103395b/torcharch/conv.py

Feature Pyramid Networks for Object Detection

https://github.com/adept-thu/SATGCN

S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection

https://github.com/aditbiswas1/P3-behavioral-cloning

End to End Learning for Self-Driving Cars

https://github.com/adityaarun1/pytorch_fast-er_rcnn

Feature Pyramid Networks for Object Detection

https://github.com/adldotori/ResNet

Deep Residual Learning for Image Recognition

https://github.com/adverml/advopenood

Generalized Out-of-Distribution Detection: A Survey

https://github.com/agarwa65/lidar_camera_calibration

LiDAR-Camera Calibration using 3D-3D Point correspondences

https://github.com/agassi4013/Log-DenseNet-Tensorflow

Densely Connected Convolutional Networks

https://github.com/agelencs/darknet_original

YOLOv3: An Incremental Improvement

https://github.com/agutuyen-dev/darknet

Focal Loss for Dense Object Detection

https://github.com/ahhan02/darknet-alex

Focal Loss for Dense Object Detection

https://github.com/ahmadelsallab/MultiCheXNet

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/ahmadelsallab/lidargan

LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving

https://github.com/ahq1993/MPNet

Motion Planning Networks

https://github.com/ai4ce/FLAT

Fooling LiDAR Perception via Adversarial Trajectory Perturbation

https://github.com/aibeedetect/bfjdet

CityPersons: A Diverse Dataset for Pedestrian Detection

https://github.com/aidriver/ChauffeurNet

ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

https://github.com/aim-uofa/AdelaiDet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/aim-uofa/adet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/aimbsg/EVA4_S15

Densely Connected Convolutional Networks

https://github.com/aimerykong/RGBT-detection

Multimodal Object Detection via Probabilistic Ensembling

https://github.com/aimotive/mm_training

aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.