Papers with Code 아카이브
3D Lane Detection
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The goal of 3D Lane Detection is to perceive lanes that provide guidance for autonomous vehicles. A lane can be represented as a visible laneline or a conceptual centerline. Furthermore, a lane obtains extra attributes from the understanding of the surrounding environment.
<span style="color:grey; opacity: 0.6">( Image credit: OpenLane-V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/OpenDriveLab/OpenLane-V2
공식 코드OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
https://github.com/OpenDriveLab/PersFormer_3DLane
공식 코드PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
https://github.com/SAIC-Vision/WS-3D-Lane
공식 코드WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
https://github.com/core9724/awesome-lane-detection
공식 코드https://github.com/doo-do/fhld
공식 코드https://github.com/homothetic/topo2d
공식 코드Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane Priors
https://github.com/hustvl/maptr
공식 코드MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
https://github.com/jbji/repvf
공식 코드RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
https://github.com/jingy1yu/scalablemap
공식 코드ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction
https://github.com/jmoonr/dv-3dlane
공식 코드DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
https://github.com/jmoonr/latr
공식 코드LATR: 3D Lane Detection from Monocular Images with Transformer
https://github.com/liuruijin17/clgo
공식 코드Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints
https://github.com/megvii-research/petr
공식 코드PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
https://github.com/nv-tlabs/cosmos-drive-dreams
공식 코드Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
https://github.com/once-3dlanes/once_3dlanes_benchmark
공식 코드https://github.com/opendrivelab/toponet
공식 코드https://github.com/runkaizhao/liladet
공식 코드https://github.com/tusen-ai/anchor3dlane
공식 코드Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection
https://github.com/wudongming97/topomlp
공식 코드TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection
공식 코드Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection
https://github.com/OpenDriveLab/OpenLane
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
https://github.com/Tsinghua-MARS-Lab/vectormapnet
https://github.com/tsinghua-mars-lab/online-hd-map-construction-cvpr2023
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.