https://github.com/JLrumberger/SpatialEmbeddingLoss
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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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/Jacoo-Zhao/3D-Pose-Based-Feedback-For-Physical-Exercises
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/Jasonkks/mlcnet
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
https://github.com/JdeRobot/BehaviorSuite
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/JdeRobot/NeuralBehaviors
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/Jhonve/jhonve.github.io
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
https://github.com/JuliaChae/M3D-RPN-Waymo
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/Jun-CEN/Open-World-Semantic-Segmentation
https://github.com/JunweiLiang/Object_Detection_Tracking
From Recognition to Prediction: Analysis of Human Action and Trajectory Prediction in Video
https://github.com/JunweiLiang/social-distancing-prediction
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
https://github.com/KarhouTam/FL-bench
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
https://github.com/LKLQQ/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/LONG-9621/Derivative_Efficient
https://github.com/LONG-9621/Generating-3D-Adversarial-Point-Clouds
https://github.com/LT1st/SmallObstacleDetection/blob/main/README.md
https://github.com/LakshmiTeja17/NNFL-Project
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
https://github.com/Lawhy/Searchive
https://github.com/Li-Chengyang/MSDS-RCNN
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
https://github.com/Linghao-Yang/Road-Map-SLAM
RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving
https://github.com/Lxrd-AJ/Advanced_ML
A Review on Deep Learning Techniques Applied to Semantic Segmentation
https://github.com/MCG-NJU/VideoChat-Online
https://github.com/MK-tech20/CGA2M_plus
Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
https://github.com/MS-Mind/MS-Code-08/tree/main/MGN
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
https://github.com/Madhusakth/Adaptive-Radar-Acquisition
https://github.com/Manjuphoenix/IndraEye
https://github.com/Manojbhat09/DATF-Trajformer
Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving
https://github.com/MarvinTeichmann/KittiBox
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/MarvinTeichmann/KittiSeg
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/MegEngine/YOLOX
https://github.com/Mind23-2/MindCode-180
https://github.com/Mind23-2/MindCode-21
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
https://github.com/Mind23-2/MindCode-23
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
https://github.com/MindCode-4/code-12/tree/main/multimodal-trajectory
https://github.com/MindCode-4/code-3/tree/main/flava
FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
https://github.com/MindCode-4/code-8/tree/main/multimodal-trajectory
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/MindSpore-paper-code-2/code2/tree/main/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.