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/poodarchu/Class-balanced-Grouping-and-Sampling-for-Point-Cloud-3D-Object-Detection

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Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

https://github.com/poodarchu/efg

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TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses

https://github.com/potsui99/unirgb-ir

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UniRGB-IR: A Unified Framework for RGB-Infrared Semantic Tasks via Adapter Tuning

https://github.com/poudel-bibek/Urban-Control

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Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning

https://github.com/ppauli/2D-LipCNNs

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LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers

https://github.com/pramodv1993/VAST

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Visual Probing and Correction of Object Recognition Models with Interactive user feedback

https://github.com/pranav-chib/end-to-end-autonomous-driving

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Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey

https://github.com/pranav-chib/recent-advancements-in-end-to-end-autonomous-driving-using-deep-learning

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Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey

https://github.com/praveen-palanisamy/macad-gym

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Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning

https://github.com/prbonn/3diss

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Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving

https://github.com/prbonn/4dmos

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Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions

https://github.com/prbonn/4dndf

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3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation

https://github.com/prbonn/contmav

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Open-World Semantic Segmentation Including Class Similarity

https://github.com/prbonn/lidiff

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Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion

https://github.com/princeton-computational-imaging/DirtyPixels

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Dirty Pixels: Towards End-to-End Image Processing and Perception

https://github.com/princeton-computational-imaging/SeeingThroughFog

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Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

https://github.com/princeton-computational-imaging/ZeroScatter

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ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media

https://github.com/prishita123/curriculum_rl_for_driving

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Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning

https://github.com/prismformore/multi-task-transformer

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Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation

https://github.com/priyankkalgaonkar/Image-Classification-with-CondenseNeXt-for-ARM-Based-Computing-Platforms

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Image Classification with CondenseNeXt for ARM-Based Computing Platforms

https://github.com/probabilistic-jsmas/probabilistic-jsmas

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Probabilistic Jacobian-based Saliency Maps Attacks

https://github.com/proroklab/xaer

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Explanation-Aware Experience Replay in Rule-Dense Environments

https://github.com/prs-eth/DeFlow

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DEFLOW: Self-supervised 3D Motion Estimation of Debris Flow

https://github.com/prs-eth/PCAccumulation

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Dynamic 3D Scene Analysis by Point Cloud Accumulation

https://github.com/psi-intention2022/psi-intention

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PSI: A Pedestrian Behavior Dataset for Socially Intelligent Autonomous Car

https://github.com/psosnin/AbstractGradientTraining

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Certified Robustness to Data Poisoning in Gradient-Based Training

https://github.com/public-bots/gaussianpretrain

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GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous Driving

https://github.com/public-bots/tigdistill-bev

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TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation

https://github.com/pulp-platform/pulp-dronet

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A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

https://github.com/purduedigitaltwin/lampilot

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LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs

https://github.com/purduedigitaltwin/seneva

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Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture

https://github.com/pxiangwu/MotionNet

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

https://github.com/pyun-ram/FL3D

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Focal Loss in 3D Object Detection

https://github.com/qcraftai/cross-modal-ssl

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Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds

https://github.com/qcraftai/distill-bev

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DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation

https://github.com/qcraftai/pillar-motion

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Self-Supervised Pillar Motion Learning for Autonomous Driving

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