https://github.com/tflinkow/fmas2024
공식 코드Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report
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/tfy14esa/point-slam
공식 코드https://github.com/tfzhou/vs-survey
공식 코드https://github.com/th1nkmore/carla_dataset_tools
공식 코드https://github.com/thaipduong/kernelbasedmap
공식 코드Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
https://github.com/thanif/CPL
공식 코드https://github.com/thanif/Camera-Calibration-through-Camera-Projection-Loss
공식 코드https://github.com/thedeepchecker/thedeepchecker
공식 코드https://github.com/theia-4869/bicross
공식 코드Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
https://github.com/thierryderuyttere/talk2car-destination
공식 코드Predicting Physical World Destinations for Commands Given to Self-Driving Cars
https://github.com/thinklab-sjtu/awesome-llm4ad
공식 코드LLM4Drive: A Survey of Large Language Models for Autonomous Driving
https://github.com/thinklab-sjtu/bench2drivezoo
공식 코드Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
https://github.com/thinklab-sjtu/drivetransformer
공식 코드DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
https://github.com/thinkxca/3dgs
공식 코드3D Gaussian Splatting against Moving Objects for High-Fidelity Street Scene Reconstruction
https://github.com/thomasverelst/blockcopy-video-processing-pytorch
공식 코드https://github.com/thu-ml/oddefense
공식 코드On the Importance of Backbone to the Adversarial Robustness of Object Detectors
https://github.com/thu17cyz/3DIoUMatch
공식 코드3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
https://github.com/thuml/ContextWM
공식 코드Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
https://github.com/tiagocortinhal/titan-next
공식 코드https://github.com/tianweiy/CenterPoint
공식 코드https://github.com/tianweiy/MVP
공식 코드https://github.com/tianzhi0549/FCOS
공식 코드https://github.com/tier4/road_lane_network_graph_open
공식 코드Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision
https://github.com/tiffamy/3dod-lsf
공식 코드Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant Features
https://github.com/tileb1/motion-prediction-tim
공식 코드https://github.com/timbroed/MUSES
공식 코드MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
https://github.com/timbroed/cafuser
공식 코드CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes
https://github.com/timbroed/hrfuser
공식 코드HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection
https://github.com/tinh2044/YOLO12-UnderWater
공식 코드Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation
https://github.com/tinker-twins/autorace-simulator
공식 코드Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
https://github.com/tinyvision/SOLIDER
공식 코드https://github.com/tjradarlab/tj4dradset
공식 코드https://github.com/tjubiit/TJU-DHD
공식 코드From Handcrafted to Deep Features for Pedestrian Detection: A Survey
https://github.com/tlabarta/helpfulnessofxai
공식 코드Study on the Helpfulness of Explainable Artificial Intelligence
https://github.com/tldrafael/pisss
공식 코드https://github.com/toeleot/nero
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.