https://github.com/wvangansbeke/LaneDetection_End2End
공식 코드End-to-end Lane Detection through Differentiable Least-Squares Fitting
Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/wvangansbeke/Sparse-Depth-Completion
공식 코드Sparse and noisy LiDAR completion with RGB guidance and uncertainty
https://github.com/wwh0411/fedrsu
공식 코드FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
https://github.com/wyddmw/WiseAD
공식 코드WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model
https://github.com/wzm5853/bench4merge
공식 코드https://github.com/wzzheng/doe
공식 코드Doe-1: Closed-Loop Autonomous Driving with Large World Model
https://github.com/wzzheng/gaussianad
공식 코드https://github.com/wzzheng/genad
공식 코드https://github.com/wzzheng/gpd
공식 코드https://github.com/wzzheng/hass
공식 코드Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection
https://github.com/wzzheng/occsora
공식 코드OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving
https://github.com/wzzheng/occworld
공식 코드OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
https://github.com/wzzheng/pointocc
공식 코드PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction
https://github.com/wzzheng/stag
공식 코드Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model
https://github.com/wzzheng/tpvformer
공식 코드Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction
https://github.com/xaviergrool/fgfusion
공식 코드FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection
https://github.com/xccyue/MutualDistance
공식 코드Scene-aware Human Motion Forecasting via Mutual Distance Prediction
https://github.com/xdeng7/nightlab
공식 코드NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night
https://github.com/xh-liu/HydraPlus-Net
공식 코드HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
https://github.com/xhwind/v2xp-asg
공식 코드V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception
https://github.com/xiandaguo/drive-mllm
공식 코드https://github.com/xiandaguo/openstereo
공식 코드OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline
https://github.com/xiangbogaobarry/mambast
공식 코드MambaST: A Plug-and-Play Cross-Spectral Spatial-Temporal Fuser for Efficient Pedestrian Detection
https://github.com/xiangchong1/3d-adv-pc
공식 코드https://github.com/xiangxu-0103/lima
공식 코드https://github.com/xiangxu-0103/superflow
공식 코드4D Contrastive Superflows are Dense 3D Representation Learners
https://github.com/xiaoaoran/3d_url_survey
공식 코드Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
https://github.com/xiaoaoran/semanticstf
공식 코드3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds
https://github.com/xiaogangpeng/tbiformer
공식 코드Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting
https://github.com/xiaohao-xu/slam-under-perturbation
공식 코드Customizable Perturbation Synthesis for Robust SLAM Benchmarking
https://github.com/xiaohao-xu/unified-pretrain-ad
공식 코드https://github.com/xiaohaoChen/rrc_detection
공식 코드Accurate Single Stage Detector Using Recurrent Rolling Convolution
https://github.com/xiaolongtang23/hpnet
공식 코드HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
https://github.com/xiaolul2/uigenmap
공식 코드Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction
https://github.com/xiaomi-mlab/mila.github.io
공식 코드MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Driving
https://github.com/xiaomi/cgnet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.