https://github.com/FilippoAleotti/mobilePydnet
공식 코드Real-time single image depth perception in the wild with handheld devices
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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Autonomous driving is the task of driving a vehicle 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: 0.5">(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ForzaETH/LLMxRobot
공식 코드Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models
https://github.com/Fredrik00/avod
공식 코드https://github.com/GUO-W/MultiMotion
공식 코드https://github.com/GangZhang842/CPGNet
공식 코드CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
https://github.com/GilgameshD/ICRA2020-CMTS
공식 코드CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
https://github.com/HEmile/neurosymbolic-diffusion
공식 코드https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN
공식 코드Stereo R-CNN based 3D Object Detection for Autonomous Driving
https://github.com/HKUST-Aerial-Robotics/VINS-Mono
공식 코드Online Temporal Calibration for Monocular Visual-Inertial Systems
https://github.com/HMRC-AEL/MonoDEVSNet
공식 코드Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision
https://github.com/HMX2013/Covex-hull-based-fitting-3D-LiDAR-ROS
공식 코드An Efficient Convex Hull-based Vehicle Pose Estimation Method for 3D LiDAR
https://github.com/HalleyJiang/DiPE
공식 코드https://github.com/HanLingsgjk/CSCV
공식 코드ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video
https://github.com/HanjiangHu/DASGIL
공식 코드DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization
https://github.com/HanjiangHu/DIFL-FCL
공식 코드Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments
https://github.com/HanjiangHu/DISAM
공식 코드https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection
공식 코드Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
https://github.com/Haoran-SONG/PiP-Planning-informed-Prediction
공식 코드PiP: Planning-informed Trajectory Prediction for Autonomous Driving
https://github.com/Harry1993/GhostImage
공식 코드GhostImage: Remote Perception Attacks against Camera-based Image Classification Systems
https://github.com/HensoldtOptronicsCV/MultispectralPedestrianDetection
공식 코드https://github.com/HimangiM/Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation
공식 코드Just Go with the Flow: Self-Supervised Scene Flow Estimation
https://github.com/HjalmarWijk/bounded-prescience
공식 코드https://github.com/Hoangpham13579/end_to_end_anomaly_detection
공식 코드Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System
https://github.com/HuangJunJie2017/BEVDet
공식 코드BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
https://github.com/I2-Multimedia-Lab/IPDAE
공식 코드IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
https://github.com/I2RDL2/ASTAR-3D
공식 코드A*3D Dataset: Towards Autonomous Driving in Challenging Environments
https://github.com/ICEORY/PMF
공식 코드EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation
https://github.com/ISEE-Technology/lidar-with-velocity
공식 코드https://github.com/ITSEG-MQ/Adv-attack-and-defense-on-driving-model
공식 코드An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
https://github.com/ITSEG-MQ/Sensor-Fusion-Against-VoiceCommand-Attacks
공식 코드Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles
https://github.com/IVIPLab/FBSNet
공식 코드FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation
https://github.com/Infernolia/WEDGE
공식 코드WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models
https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems
공식 코드Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems
https://github.com/InterDigitalInc/FESTA
공식 코드FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds
https://github.com/JOP-Lee/READ-Large-Scale-Neural-Scene-Rendering-for-Autonomous-Driving
공식 코드READ: Large-Scale Neural Scene Rendering for Autonomous Driving
https://github.com/James-Yuichi-Sato/MLSYS-2022-Video-Pipeline
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.