https://github.com/northsummer/lgkd
공식 코드BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 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/northsummer/sliceocc
공식 코드SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation
https://github.com/notacracker/cotr
공식 코드COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction
https://github.com/noyzzz/emap
공식 코드Ego-Motion Aware Target Prediction Module for Robust Multi-Object Tracking
https://github.com/nrhine1/deep_imitative_models
공식 코드Deep Imitative Models for Flexible Inference, Planning, and Control
https://github.com/nubot-nudt/bevdiffloc
공식 코드BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion Model
https://github.com/nubot-nudt/segnet4d
공식 코드SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud
https://github.com/nullmax-vision/hip-ad
공식 코드https://github.com/nullmax-vision/simpb
공식 코드SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras
https://github.com/nutonomy/nuscenes-devkit
공식 코드https://github.com/nutonomy/second.pytorch
공식 코드PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/nvidia/tensorrt-llm
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/nvlabs/bev-planner
공식 코드Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
https://github.com/nvlabs/fb-bev
공식 코드FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
https://github.com/nvlabs/gtrs
공식 코드Generalized Trajectory Scoring for End-to-end Multimodal Planning
https://github.com/nvlabs/hydra-mdp
공식 코드Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
https://github.com/nvlabs/omnidrive
공식 코드OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
https://github.com/nvlabs/smerf
공식 코드Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
https://github.com/nvlabs/trajdata
공식 코드trajdata: A Unified Interface to Multiple Human Trajectory Datasets
https://github.com/o-vigia/gma3d
공식 코드GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow
https://github.com/olga-zats/gtda
공식 코드Gated Temporal Diffusion for Stochastic Long-Term Dense Anticipation
https://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/onlytailei/carla_cil_pytorch
공식 코드Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
https://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/opendrivelab/bevperception-survey-recipe
공식 코드Geometric-aware Pretraining for Vision-centric 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.