https://github.com/opendrivelab/birds-eye-view-perception
공식 코드Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
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/opendrivelab/driveadapter
공식 코드https://github.com/opendrivelab/driveagi
공식 코드Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future
https://github.com/opendrivelab/drivelm
공식 코드https://github.com/opendrivelab/elm
공식 코드https://github.com/opendrivelab/end-to-end-autonomous-driving
공식 코드https://github.com/opendrivelab/ppgeo
공식 코드Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling
https://github.com/opendrivelab/thinktwice
공식 코드Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
https://github.com/opendrivelab/toponet
공식 코드https://github.com/opendrivelab/uniad
공식 코드https://github.com/opendrivelab/vidar
공식 코드Visual Point Cloud Forecasting enables Scalable Autonomous Driving
https://github.com/opendrivelab/vista
공식 코드Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
https://github.com/opengvlab/drivemlm
공식 코드https://github.com/opengvlab/humanbench
공식 코드HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
https://github.com/opengvlab/internvl
공식 코드Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance
https://github.com/openlka/openlka
공식 코드https://github.com/openperceptionx/hdgt
공식 코드https://github.com/openvinotoolkit/training_extensions
공식 코드ASL Recognition with Metric-Learning based Lightweight Network
https://github.com/oravus/DeltaDescriptors
공식 코드Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
https://github.com/oravus/seqNet
공식 코드SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition
https://github.com/orlitany/Cloud_dictionary
공식 코드Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
https://github.com/os-hxfan/bayesnn_fpga
공식 코드When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA
https://github.com/os-hxfan/mcme_fpga_acc
공식 코드Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA
https://github.com/oskarnatan/deepipc
공식 코드DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
https://github.com/oskarnatan/deepipcv2
공식 코드https://github.com/oskarnatan/end-to-end-driving
공식 코드End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent
https://github.com/othmanela/certified_segmentation
공식 코드https://github.com/ov2slam/ov2slam
공식 코드OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications
https://github.com/owen-liuyuxuan/visionfactory
공식 코드Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous Driving
https://github.com/ozzyou/3d-avs
공식 코드https://github.com/p-shekhar/adversarial-patch-transferability
공식 코드Cross-Model Transferability of Adversarial Patches in Real-time Segmentation for Autonomous Driving
https://github.com/pagand/e2etransfuser
공식 코드https://github.com/pandapan01/evssc
공식 코드https://github.com/panmt/iso-dream
공식 코드Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models
https://github.com/panmt/mbrl_with_isolated_imaginations
공식 코드Model-Based Reinforcement Learning with Isolated Imaginations
https://github.com/pay246-git468/radarnext
공식 코드RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging Radar
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.