https://github.com/olga-zats/gtda
공식 코드Gated Temporal Diffusion for Stochastic Long-Term Dense Anticipation
Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 15
- 평가표
- 1
- 보존된 평가 기록
- 2
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/olivesgatech/CURE-TSD
공식 코드https://github.com/olly-styles/Multiple-Object-Forecasting
공식 코드Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
https://github.com/oloocki/tum-facade
공식 코드TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for façade segmentation
https://github.com/omarathon/rl-multi-agent-car-parking
공식 코드https://github.com/once-3dlanes/once_3dlanes_benchmark
공식 코드https://github.com/oneday88/kddcup2021cbcbingo
공식 코드https://github.com/onlytailei/carla_cil_pytorch
공식 코드Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
https://github.com/onlytailei/gym_ped_sim
공식 코드https://github.com/oooolga/Ctrl-V
공식 코드Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion
https://github.com/open-air-sun/mars
공식 코드MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving
https://github.com/open-mmlab/OpenPCDet
공식 코드MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection
https://github.com/open-mmlab/mmdetection3d
공식 코드Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/opencalib/lidar2camera_self-check
공식 코드https://github.com/opencalib/lidar2lidar
공식 코드CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene
https://github.com/opencalib/sensorx2car
공식 코드SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios
https://github.com/opendilab/InterFuser
공식 코드Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
https://github.com/opendilab/lmdrive
공식 코드LMDrive: Closed-Loop End-to-End Driving with Large Language Models
https://github.com/opendilab/smartrefine
공식 코드SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
https://github.com/opendrivelab/bevperception-survey-recipe
공식 코드Geometric-aware Pretraining for Vision-centric 3D Object Detection
https://github.com/opendrivelab/birds-eye-view-perception
공식 코드Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
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/hulk
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.