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/tu-rbo/concarne

Learning image representations tied to ego-motion

https://github.com/tuananh1007/Faster-R-CNN-and-Mask-R-CNN-in-PyTorch-1.0

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/tub-rip/dvs_mcemvs

Event-based Stereo Depth Estimation: A Survey

https://github.com/tub-rip/es-ptam

Event-based Stereo Depth Estimation: A Survey

https://github.com/tue-mps/vfm-uda

What is the Added Value of UDA in the VFM Era?

https://github.com/tuncalie/sim-atav

Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components

https://github.com/tuscan-chicken-wrap/ECE260FinalProject

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/ucbdrive/3d-vehicle-tracking

Joint Monocular 3D Vehicle Detection and Tracking

https://github.com/uclaml/Padam

Deep Residual Learning for Image Recognition

https://github.com/uditsharma29/flynet-cann

A Hybrid Compact Neural Architecture for Visual Place Recognition

https://github.com/ugururesin/CarND-Behavioral-Cloning

End to End Learning for Self-Driving Cars

https://github.com/ugururesin/Udacity-CarND-Behavioral-Cloning

End to End Learning for Self-Driving Cars

https://github.com/ultralytics/yolov3

YOLOv3: An Incremental Improvement

https://github.com/ultralytics/yolov5

Deep Residual Learning for Image Recognition

https://github.com/umautobots/bidireaction-trajectory-prediction

BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation

https://github.com/umautobots/failing-to-learn

Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars

https://github.com/umich-curly/3dmapping

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

https://github.com/umich-curly/bki_ros

RELLIS-3D Dataset: Data, Benchmarks and Analysis

https://github.com/unmannedlab/point_labeler

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

https://github.com/unsky/FPN

Feature Pyramid Networks for Object Detection

https://github.com/unsky/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/unsky/focal-loss

Focal Loss for Dense Object Detection

https://github.com/unveilx/slm-od-ml-comparison

Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset

https://github.com/usnistgov/image-classification-resnet50

Deep Residual Learning for Image Recognition

https://github.com/usnistgov/image-regression-resnet50

Deep Residual Learning for Image Recognition

https://github.com/usnistgov/object-detection-yolov3

YOLOv3: An Incremental Improvement

https://github.com/ut-amrl/pips

Robot Action Selection Learning via Layered Dimension Informed Program Synthesis

https://github.com/ut-austin-rpl/coopernaut

COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

https://github.com/utiasasrl/crystal_ball_nav

Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation

https://github.com/utiasasrl/utin3d

Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation

https://github.com/uw-hai/limeade

Deep Residual Learning for Image Recognition

https://github.com/vahidkhosh/squeezeDet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/vahidkhosh/squeezedet-keras

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/valeoai/NeeDrop

NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping

https://github.com/valeoai/pointbev

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

https://github.com/vantupham/darknet

Focal Loss for Dense Object Detection

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