https://github.com/hongru-seu/PointPilliar
PointPillars: Fast Encoders for Object Detection from Point Clouds
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/hova88/Lidardet
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/hova88/PointPillars_MultiHead_40FPS
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/huang-xx/STGAT
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/huawei-noah/vega
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
https://github.com/huixiancheng/no-cpgnet
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
https://github.com/hula-ai/mc_dropconnect
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
https://github.com/hustvl/YOLOP
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/hyzhou404/hugsim
HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving
https://github.com/iPhoring/BehavioralCloning
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/iac-team/semseg
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/ibaiGorordo/ONNX-HybridNets-Multitask-Road-Detection
https://github.com/ibaiGorordo/PyTorch-High-Res-Stereo-Depth-Estimation
https://github.com/idsia-robotics/RandLA-Net-pytorch
https://github.com/idthanm/env_build
https://github.com/inhwanbae/npsn
https://github.com/intel/ad-rss-lib
https://github.com/inverted-ai/torchdrivesim
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
https://github.com/irh-ca-team-car/attention-data
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/isl-org/Open3D-ML
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/isunLt/PolarSeg
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
https://github.com/jchengai/planTF
https://github.com/jcheunglin/Full-Resolution-Residual-Networks-with-PyTorch
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
https://github.com/jeffwang987/drivedreamer
DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
https://github.com/jeongjin0/detector-cleanse
https://github.com/jesse01/paddle-yolox
https://github.com/jhkim-spa/FVNet_v2
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/jhonve/implicitpcda
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
https://github.com/jiaxue1993/pytorch-material-classification
Differential Viewpoints for Ground Terrain Material Recognition
https://github.com/jinlong17/CoMamba
CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
https://github.com/jinlong17/advgps
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
https://github.com/jinsheng124/yolox
https://github.com/jjdiep/Multi-Object-Tracking
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
https://github.com/johnwlambert/argoverse_cbgs_kf_tracker
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
https://github.com/jostl/masters-thesis
https://github.com/juglab/EmbedSeg
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.