https://github.com/tensorflow/lingvo
공식 코드DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
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/testingautomated-usi/DeepJanus
공식 코드Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing
https://github.com/testingautomated-usi/genbo
공식 코드Boundary State Generation for Testing and Improvement of Autonomous Driving Systems
https://github.com/testingautomated-usi/maxitwo
공식 코드Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing
https://github.com/tfzhou/vs-survey
공식 코드https://github.com/th1nkmore/carla_dataset_tools
공식 코드https://github.com/thanif/CPL
공식 코드https://github.com/thanif/Camera-Calibration-through-Camera-Projection-Loss
공식 코드https://github.com/theia-4869/bicross
공식 코드Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
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/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/tianweiy/CenterPoint
공식 코드https://github.com/tianweiy/MVP
공식 코드https://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/cafuser
공식 코드CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes
https://github.com/tjradarlab/tj4dradset
공식 코드https://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 코드https://github.com/tong-zeng/dvbench
공식 코드https://github.com/tongnie/awesome-llm4tr
공식 코드https://github.com/torrvision/straighttoshapes
공식 코드Straight to Shapes++: Real-time Instance Segmentation Made More Accurate
https://github.com/tourlics/robus_dataset
공식 코드RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation
https://github.com/towardsautonomy/VR3Dense
공식 코드https://github.com/towardsautonomy/datasetequity
공식 코드DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets
https://github.com/toytiny/radarocc
공식 코드RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar
https://github.com/tpoellabauer/ycb-luma
공식 코드YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization
https://github.com/trailab/hypermodest
공식 코드HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering
https://github.com/trailab/jdt3d
공식 코드JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention
https://github.com/trailab/pdv
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.