Papers with Code 아카이브

Autonomous Vehicles

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하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

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/csiro-robotics/uncertainty-lpr

STUN: Self-Teaching Uncertainty Estimation for Place Recognition

https://github.com/csm-kr/Retinanet_pytorch

Focal Loss for Dense Object Detection

https://github.com/csmiler/tf-faster-rcnn-cpu

Feature Pyramid Networks for Object Detection

https://github.com/csqiangwen/Video_Waterdrop_Removal_in_Driving_Scenes

Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes

https://github.com/cuifc1997/nuscene-study

nuScenes: A multimodal dataset for autonomous driving

https://github.com/cuijiaxun/MITC-Project

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/cuonga1cvp/yolov3_cuong

YOLOv3: An Incremental Improvement

https://github.com/cv-team/lane_detection_ML

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/cvg/Hierarchical-Localization

From Coarse to Fine: Robust Hierarchical Localization at Large Scale

https://github.com/cxmomo/Neighbor-Vote

Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting

https://github.com/cyanamous/Self-Driving-Car-

End to End Learning for Self-Driving Cars

https://github.com/cyctrung/DPnet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/czyczyyzc/CondLSTR

https://github.com/d-eremeev/SC-GAN

Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization

https://github.com/dC4rlos/TFM

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/dablack2020/Seniordes

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

https://github.com/dadung/G2D

G2D: from GTA to Data

https://github.com/daeheepark/PathPredictNusc

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

https://github.com/daeheepark/t4p

trajdata: A Unified Interface to Multiple Human Trajectory Datasets

https://github.com/dajiangsuo/EV_mixtedAutonomy

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/dangWV/yolov3_darknet53

YOLOv3: An Incremental Improvement

https://github.com/danielamassiceti/CCA-visualdialogue

Deep Residual Learning for Image Recognition

https://github.com/danielparada1/RL_SpaceInvaders

Deep Residual Learning for Image Recognition

https://github.com/danijc8196/MyRosAirSim

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

https://github.com/dannofield/CarND-Extended-Kalman-Filter

No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs

https://github.com/dannofield/Particle-Filter-Kidnapped-Vehicle

Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality

https://github.com/dansthemanwhosakid/image-classification-cancer

Why do linear SVMs trained on HOG features perform so well?

https://github.com/danxuhk/CMT-CNN

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

https://github.com/darkAlert/yolov3-rt

YOLOv3: An Incremental Improvement

https://github.com/darpa-l2m/l2metrics

Lifelong Learning Metrics

https://github.com/darshans0200/YOLOTest

Focal Loss for Dense Object Detection

https://github.com/datoboat/Vehicle-Detection

Deep Residual Learning for Image Recognition

https://github.com/daveboat/pytorch_focal_loss

Focal Loss for Dense Object Detection

https://github.com/davyneven/fastSceneUnderstanding

Fast Scene Understanding for Autonomous Driving

https://github.com/dawn360/Behavioral-Cloning-P3

End to End Learning for Self-Driving Cars

https://github.com/daxiapazi/faster-rcnn

Feature Pyramid Networks for Object Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.