https://github.com/HanjiangHu/DIFL-FCL
공식 코드Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments
Papers with Code 아카이브
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/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/HannahHaensen/pyfu
공식 코드Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation
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/HarshayuGirase/PECNet
공식 코드It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
https://github.com/Hatmm/PED-DETR-for-Pedestrian-Detection
공식 코드https://github.com/HensoldtOptronicsCV/MultispectralPedestrianDetection
공식 코드https://github.com/Herrccc/DR-TANet
공식 코드DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection
https://github.com/HiEST/cova-tuner
공식 코드Towards Automatic Model Specialization for Edge Video Analytics
https://github.com/HiEST/edgeautotuner
공식 코드Towards Automatic Model Specialization for Edge Video Analytics
https://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/HyHuang1995/gmmloc
공식 코드https://github.com/Hydragon516/ESA-official
공식 코드https://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/ICONgroupCWC/Real-Time_VR_RemoteControl
공식 코드Real-Time Remote Control via VR over Limited Wireless Connectivity
https://github.com/IRMVLab/SemGauss-SLAM
공식 코드https://github.com/IRVLab/so_dso_place_recognition
공식 코드A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors
https://github.com/ISEE-Technology/lidar-with-velocity
공식 코드https://github.com/ISorokos/SafeML
공식 코드Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring
https://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/IcebergKnight/NMS-Loss
공식 코드NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
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/IvyYZ/POAR
공식 코드POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
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/JWFangit/LOTVS-DADA
공식 코드DADA: Driver Attention Prediction in Driving Accident Scenarios
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.