https://github.com/rickyHong/AirSim-Simulator-repl
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Formal Security Analysis of Neural Networks using Symbolic Intervals
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
Contrastive Learning for Lane Detection via cross-similarity
Enhanced free space detection in multiple lanes based on single CNN with scene identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.