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/wagner-group/demystify-random-transform
공식 코드Demystifying the Adversarial Robustness of Random Transformation Defenses
https://github.com/walzimmer/3d-bat
공식 코드https://github.com/wang-chen/airloop
공식 코드https://github.com/wang-chen/correlation_flow
공식 코드Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
https://github.com/wang-jh18-svm/griffin
공식 코드Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark
https://github.com/wangguojun2018/CenterNet3d
공식 코드https://github.com/wangguojun2018/MVAF-Net
공식 코드https://github.com/wangjw18/LCC
공식 코드Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability
https://github.com/wangsen99/FDLNet
공식 코드https://github.com/wangsh0111/iftr
공식 코드IFTR: An Instance-Level Fusion Transformer for Visual Collaborative Perception
https://github.com/wangwenxi-handsome/joint-multipathpp
공식 코드https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List
공식 코드https://github.com/wangyueft/detr3d
공식 코드DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
https://github.com/wangzc0422/c2f-tp
공식 코드C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction
https://github.com/wanifarooq/ncod
공식 코드Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
https://github.com/waterscenes/waterscenes
공식 코드https://github.com/watonomous/action-classification
공식 코드RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow
https://github.com/wayveai/driving-with-llms
공식 코드Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
https://github.com/wayveai/fiery
공식 코드FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
https://github.com/wayveai/lingoqa
공식 코드https://github.com/wayveai/mile
공식 코드https://github.com/wayveai/socs
공식 코드Linking vision and motion for self-supervised object-centric perception
https://github.com/wayveai/wayve_scenes
공식 코드WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving
https://github.com/wei-mao-2019/LearnTrajDep
공식 코드Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/wei-mao-2019/contawaremotionpred
공식 코드https://github.com/weibinkou/crchfl
공식 코드https://github.com/weijiemax/lssinst
공식 코드LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance Representation
https://github.com/weinajin/euca
공식 코드https://github.com/weiqingq/CLRKDNet
공식 코드CLRKDNet: Speeding up Lane Detection with Knowledge Distillation
https://github.com/weiyangdaren/streamdsgn-pytorch
공식 코드Real-time Stereo-based 3D Object Detection for Streaming Perception
https://github.com/weiyithu/surrounddepth
공식 코드SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation
https://github.com/weiyithu/surroundocc
공식 코드SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving
https://github.com/wenc13/patchgeneration
공식 코드Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach
https://github.com/wenjie710/PivotNet
공식 코드PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction
https://github.com/wenyyu/IA-Seg
공식 코드Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters
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