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/iis-esslingen/vi-slam_lc_benchmark
공식 코드https://github.com/ika-rwth-aachen/occuq
공식 코드OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction
https://github.com/ikhatri/AASE
공식 코드https://github.com/iliaishacked/sponge_examples
공식 코드https://github.com/imadalishah/HSI_SemanticSegmentationModels_AD_ADAS
공식 코드https://github.com/imasmitja/rlforutracking
공식 코드https://github.com/immel-f/m3tr
공식 코드https://github.com/immel-f/sdtagnet
공식 코드SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
https://github.com/imoneoi/carla_env
공식 코드A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
https://github.com/imoneoi/onerl
공식 코드A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
https://github.com/implabunipr/movad
공식 코드https://github.com/imran2205/logicrag
공식 코드https://github.com/inspire-group/ObjectSeeker
공식 코드https://github.com/inspire-group/advml-traffic-sign
공식 코드Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos
https://github.com/intel-isl/OpenBot
공식 코드https://github.com/intelligent-control-lab/guard
공식 코드https://github.com/intelligent-control-lab/statewise_constrained_policy_optimization
공식 코드https://github.com/intelligent-vehicles/bevdriver
공식 코드BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving
https://github.com/internlandmark/landmarksystem
공식 코드https://github.com/internlm/lmdeploy
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/intrinsic-ai/collision-avoidance-metric
공식 코드https://github.com/introlab/dotmask
공식 코드https://github.com/introlab/rtabmap/tree/master/archive/2022-IlluminationInvariant
공식 코드Multi-Session Visual SLAM for Illumination Invariant Re-Localization in Indoor Environments
https://github.com/ipm-hpc/perspective-bev-transformer
공식 코드Deep Perspective Transformation Based Vehicle Localization on Bird's Eye View
https://github.com/irohxu/awesome-multimodal-llm-autonomous-driving
공식 코드A Survey on Multimodal Large Language Models for Autonomous Driving
https://github.com/islab-github/VOBEM2
공식 코드Role of Sensing and Computer Vision in 6G Wireless Communications
https://github.com/ispc-lab/lidar4d
공식 코드LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
https://github.com/ispc-lab/marl4ad
공식 코드Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
https://github.com/ispc-lab/neuralpci
공식 코드NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation
https://github.com/iurada/neutron-segmentation
공식 코드Transient Fault Tolerant Semantic Segmentation for Autonomous Driving
https://github.com/ivano-donadi/sdpr
공식 코드https://github.com/ivipsourcecode/dxslam
공식 코드DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
https://github.com/ixilai/awfusion
공식 코드All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark
https://github.com/j-cap/FL-based-neural-FF-control
공식 코드Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent Systems
https://github.com/jac99/MinkLocMultimodal
공식 코드MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.