Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/hj963434688/Faster_rcnn
https://github.com/hjr37/CG-SLAM
CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field
https://github.com/hkust-aerial-robotics/epsilon
Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor
https://github.com/hnuzhy/bpjdet
https://github.com/holytrick/AlexeyAB_darknet
https://github.com/hongkunsun/paratranscnn
https://github.com/hongru-seu/PointPilliar
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/hongyi-zhang/Fixup
https://github.com/horse007666/ResNet
https://github.com/hou-yz/multiviewx
https://github.com/hova88/Lidardet
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/hova88/PointPillars_MultiHead_40FPS
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/hq-jiang/instance-segmentation-with-discriminative-loss-tensorflow
Semantic Instance Segmentation with a Discriminative Loss Function
https://github.com/hsd1503/resnet1d
https://github.com/hsinyilin19/resnetvae
https://github.com/huaimeng97/LaneNet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/huan123/py-fatser-rcnn
https://github.com/huang-xx/STGAT
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/huangchaoxing/8536-project
https://github.com/huangchaoxing/Deep-Learning-Pilot---GTA-V
https://github.com/huawei-noah/vega
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
https://github.com/hughperkins/DeepCL
https://github.com/huixiancheng/no-cpgnet
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
https://github.com/hula-ai/mc_dropconnect
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/YOLOP
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/hustvl/sparseinst
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/hweersot/darknet-custom
https://github.com/hwillard98/htmlify-yolo-training
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hyang0129/foodclassapp
https://github.com/hycis/TensorGraph
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.