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/leehoon7/flow
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/lekhraj-d/Self-Drive-Car-Implemention-Using-Keras
https://github.com/leoandeol/cods
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientvit
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/yolox
https://github.com/lexfridman/deeptraffic
https://github.com/lh-wang/ACC
https://github.com/lhcezx/Graph-FPN
https://github.com/li-yibing/oceannet
https://github.com/lianbin/VIOSLAM
https://github.com/libiseller/MobileNetV2-dynamicFPN
https://github.com/lichuanqi/YOLOV4_Darknet
https://github.com/lightwheelai/street-gaussians-ns
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
https://github.com/liguolinhit/DynaSLAM
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
https://github.com/liguolinhit/VIORB
https://github.com/lijain/FCOS-change
https://github.com/likui01/LDNet
LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor
https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION
https://github.com/linmeeka/semanticSlam
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
https://github.com/linmeeka/slamProject
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
https://github.com/linxi159/KittiSeg
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/lionelmessi6410/ntga
https://github.com/lipengfeizju/Detection
https://github.com/lipengyuMachineLearner/FCOS
https://github.com/lippman1125/maskrcnn_benchmark_mobilenetv2
https://github.com/lishenghui/ARFL
https://github.com/littlemountainman/selfdrive
https://github.com/liuQuan98/GCL-KPConv
Density-invariant Features for Distant Point Cloud Registration
https://github.com/liuguitao/AVP-SLAM-PLUS
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot
https://github.com/liuji93/yolo_v3
https://github.com/liulianjushi/yolo_v3_tf2.0
https://github.com/liuqiang3/faster_rcnn
https://github.com/liuxiangchao369/VRS-training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.