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/srijamiya/LittleYOLO-SPP

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LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm

https://github.com/ssbin0914/Causal-Mode-Multiplexer

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Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection

https://github.com/sshaoshuai/mtr

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MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying

https://github.com/sshkhr/BigDataCup18_Submission

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Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN

https://github.com/ssloxford/ccd-signal-injection-attacks

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Signal Injection Attacks against CCD Image Sensors

https://github.com/ssloxford/short-lived-adversarial-perturbations

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SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations

https://github.com/sta8is/dino-foresight

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$\texttt{DINO-Foresight}$: Looking into the Future with DINO

https://github.com/star-center/ros_mp4

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High-Quality, ROS Compatible Video Encoding and Decoding for High-Definition Datasets

https://github.com/stefanos50/CARLA2Real

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CARLA2Real: a tool for reducing the sim2real gap in CARLA simulator

https://github.com/stepankonev/MotionCNN-Waymo-Open-Motion-Dataset

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MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving

https://github.com/stepankonev/waymo-motion-prediction-chalenge-2022-multipath-plus-plus

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MPA: MultiPath++ Based Architecture for Motion Prediction

https://github.com/stephaniedroop/av_explanations

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People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI

https://github.com/stiphyjay/lidar-ptq

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LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection

https://github.com/stiphyjay/pillartrack

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PillarTrack:Boosting Pillar Representation for Transformer-based 3D Single Object Tracking on Point Clouds

https://github.com/structurescomp/agronav

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Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection

https://github.com/stutipathak5/gps-for-point-clouds

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GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifolds

https://github.com/sty61010/crossdtr

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CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection

https://github.com/suhaisheng/difsd

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DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving

https://github.com/suhaisheng/robosense

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RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments

https://github.com/sum1lim/PU-Ray

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PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface

https://github.com/sunghoon031/LCSD_SLAM

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Loosely-Coupled Semi-Direct Monocular SLAM

https://github.com/sunh18/monosim

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MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection

https://github.com/sunjiayuanro/scfeat

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Shared Coupling-bridge for Weakly Supervised Local Feature Learning

https://github.com/sunnyhelen/jperceiver

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JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes

https://github.com/surajkothawade/talisman

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TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information

https://github.com/sustech-isus/sustechgan

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SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving

https://github.com/suwesh/parallel-perception-network

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Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing

https://github.com/svip-lab/planedepth

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PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes

https://github.com/swat-lab-optimization/RILaST

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Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems

https://github.com/swat-lab-optimization/ambiegen-tool

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AmbieGen: A Search-based Framework for Autonomous Systems Testing

https://github.com/swc-17/drivecamsim

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DriveCamSim: Generalizable Camera Simulation via Explicit Camera Modeling for Autonomous Driving

https://github.com/swc-17/sparsedrive

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SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

https://github.com/swz30/mirnetv2

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Learning Enriched Features for Fast Image Restoration and Enhancement

https://github.com/sxjyjay/msmdfusion

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MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection

https://github.com/synsin0/come

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COME: Adding Scene-Centric Forecasting Control to Occupancy World Model

https://github.com/synsin0/effocc

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EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy Network

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