https://github.com/lorebianchi98/fg-ovd
Is CLIP the main roadblock for fine-grained open-world perception?
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 4
- 보존된 평가 기록
- 23
Autonomous driving is the task of driving a vehicle 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: 0.5">(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/louisccc/sg-risk-assessment
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
https://github.com/lpiccinelli-eth/unidepth
iDisc: Internal Discretization for Monocular Depth Estimation
https://github.com/lrxiao/free-space
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/m-3lab/real3d-ad
PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features
https://github.com/m-hasan-n/pooling
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/m-serra/action-inference-for-video-prediction-benchmarking
https://github.com/malicd/LiSu
LiSu: A Dataset and Method for LiDAR Surface Normal Estimation
https://github.com/manikandan-ravikiran/squeezeDet
https://github.com/map-learning/tl2la
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/mathilde-b/SRDA
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
https://github.com/matlab-deep-learning/pretrained-salsanext
https://github.com/matsuren/HexRUNet_pytorch
Orientation-aware Semantic Segmentation on Icosahedron Spheres
https://github.com/matthew29tang/pid-model
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
https://github.com/maudzung/CenterNet3D-PyTorch
https://github.com/maudzung/Complex-YOLOv4-Pytorch
https://github.com/maudzung/RTM3D
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
https://github.com/mbhenaff/EEN
Query-Efficient Imitation Learning for End-to-End Autonomous Driving
https://github.com/mbrossar/RINS-W
https://github.com/mcederle99/MAD4QN-PS
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/mcg-nju/sparsebev
https://github.com/mcordts/cityscapesScripts
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
https://github.com/mediabrain-sjtu/collaborative-uncertainty
https://github.com/megvii-basedetection/bevdepth
MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception
https://github.com/memoatwit/universalperturbation
Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects
https://github.com/metadriverse/aco
Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
https://github.com/middleprince/YOLOX-SS
https://github.com/minoot7/Lane-Detection-Transfer-Learning
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/minxuanjun/basalt-mirror
https://github.com/minxuanjun/basalt_class
https://github.com/moabarar/nemar
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
https://github.com/mon95/centerpoint-maps
https://github.com/motional/motional-prediction-devkit
https://github.com/motional/nuplan-devkit
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
https://github.com/motokimura/PyTorch_Gaussian_YOLOv3
https://github.com/mszpc/yolox
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.