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/PINTO0309/PINTO_model_zoo/tree/main/326_YOLOPv2
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
https://github.com/PIlotcnc/neural
https://github.com/PKU-AI-Edge/DGN/
CoLight: Learning Network-level Cooperation for Traffic Signal Control
https://github.com/PPPrior/i3d-pytorch
https://github.com/PRBonn/LiDAR-MOS
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
https://github.com/PaddlePaddle/Paddle3D
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleFL
https://github.com/PaddlePaddle/PaddleOCR
https://github.com/PaddlePaddle/PaddleYOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/PaiHsuehChung/DockerYOLOV3
https://github.com/PankajKarki/Self-Driving-Car
https://github.com/PengchengAi/tf-faster-rcnn-pcai
https://github.com/Pengchengzhi/Yolov3_Own-dataset
https://github.com/Pepitaw/darknet
https://github.com/PericlesPet/darknet-demo
https://github.com/PeterBeben/continuous-K-SVD
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
https://github.com/PeterTKovacs/zold137
https://github.com/PetersonSilvadeJesus/yolov3-data-images
https://github.com/PhilippeW83440/CarND-Path-Planning-Project
https://github.com/Pointcept/Pointcept
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/PointsCoder/ONCE_Benchmark
https://github.com/PolarisAlpha/darknet
https://github.com/PolinaYA/yolov3
https://github.com/Pomu0708/PyDnet
Towards real-time unsupervised monocular depth estimation on CPU
https://github.com/Popoooo/darknet_test
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Purkialo/CrowdDet
Detection in Crowded Scenes: One Proposal, Multiple Predictions
https://github.com/Pxtri2156/AdelaiDet_v2
https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu
https://github.com/Qengineering/YoloV3-ncnn-Jetson-Nano
https://github.com/RDXiaoLu/DLNet
https://github.com/RKinDLab/ros2_psd_pcb_reloc
https://github.com/RRoundTable/OceanLitter_dataset_generator
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.