Papers with Code 아카이브

Autonomous Vehicles

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/envision-research/scale-bev

공식 코드

Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting

https://github.com/eowjd0512/fisheye-calib-adapter

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Fisheye-Calib-Adapter: An Easy Tool for Fisheye Camera Model Conversion

https://github.com/epfl-lasa/crowdbot-evaluation-tools

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Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics

https://github.com/epfl-ml4ed/interpretcc

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A Human-Centric Approach to Explainable AI for Personalized Education

https://github.com/equinoxxxxx/spn

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Sparse Prototype Network for Explainable Pedestrian Behavior Prediction

https://github.com/er-muyue/bemapnet

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End-to-End Vectorized HD-map Construction with Piecewise Bezier Curve

https://github.com/erasmo1015/vte

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On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression

https://github.com/erbloo/raan

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Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation

https://github.com/erbloo/wAP_feature_squeezing

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Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles

https://github.com/erfanaasi/unmarked_crosswalk_carla_scenario

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Classification of Time-Series Data Using Boosted Decision Trees

https://github.com/ergoncugler/q-learning

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From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no Libraries

https://github.com/eric-yyjau/pytorch-deepFEPE

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Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints

https://github.com/eriksandstroem/point-slam

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Point-SLAM: Dense Neural Point Cloud-based SLAM

https://github.com/eriksandstroem/splat-slam

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Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians

https://github.com/erosinho13/ladd

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Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning

https://github.com/esrschao/edgeregnet

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EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds

https://github.com/essoz/distributed-intersection-traffic-coordination-with-lease

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Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads

https://github.com/eth-pbl/cr3dt

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CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking

https://github.com/ethangoan/eu-seg

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Uncertainty in Real-Time Semantic Segmentation on Embedded Systems

https://github.com/ethz-asl/hf_net

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From Coarse to Fine: Robust Hierarchical Localization at Large Scale

https://github.com/ethz-asl/hfnet

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From Coarse to Fine: Robust Hierarchical Localization at Large Scale

https://github.com/ethz-asl/kalibr

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The Double Sphere Camera Model

https://github.com/ethz-asl/segmap

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SegMap: Segment-based mapping and localization using data-driven descriptors

https://github.com/ethz-asl/tsdf-plusplus

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TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

https://github.com/ethz-asl/tulip

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TULIP: Transformer for Upsampling of LiDAR Point Clouds

https://github.com/etrontech/vfmseg

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Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

https://github.com/eugenevinitsky/decentralized_bottlenecks

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Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL

https://github.com/evendrow/somoformer

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SoMoFormer: Multi-Person Pose Forecasting with Transformers

https://github.com/event-ahu/openpar

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SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm

https://github.com/event-ahu/vtf_par

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Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute Recognition

https://github.com/eventbasedvision/sevd

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SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception

https://github.com/evocargo/lidar-annotation-is-all-you-need

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Lidar Annotation Is All You Need

https://github.com/explain3d/dam

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DAM: Diffusion Activation Maximization for 3D Global Explanations

https://github.com/explain3d/lime-3d

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Surrogate Model-Based Explainability Methods for Point Cloud NNs

https://github.com/explain3d/pointcloudam

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Visualizing Global Explanations of Point Cloud DNNs

https://github.com/explainableml/bayescap

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BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

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