https://github.com/yuedumingz/PointCore
공식 코드PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/yuhengliu02/control-3d-scene
공식 코드https://github.com/yujun0-0/mma-net
공식 코드VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
https://github.com/yulin1004/mtd
공식 코드MTD: Multi-Timestep Detector for Delayed Streaming Perception
https://github.com/yumu-173/siamese-detr
공식 코드https://github.com/yunfengdiao/Skeleton-Robustness-Benchmark
공식 코드https://github.com/yunjinli/SADG-SegmentAnyDynamicGaussian
공식 코드https://github.com/yunshihuang/pmcnet
공식 코드Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
https://github.com/yurongyou/asyncdepth
공식 코드https://github.com/yurongyou/hindsight
공식 코드Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
https://github.com/yurongyou/modest
공식 코드Learning to Detect Mobile Objects from LiDAR Scans Without Labels
https://github.com/yurongyou/rote-da
공식 코드Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
https://github.com/yvanliang/DriveEditor
공식 코드https://github.com/yvanyin/drivingworld
공식 코드DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT
https://github.com/yvanyin/goalflow
공식 코드https://github.com/ywangeq/PointSeg
공식 코드PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
https://github.com/ywu0912/teamcode
공식 코드M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
https://github.com/ywyeli/unidrive
공식 코드UniDrive: Towards Universal Driving Perception Across Camera Configurations
https://github.com/yxzhang15/pcr
공식 코드Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and Taxonomy
https://github.com/yzichen/polarbevdet
공식 코드PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-View
https://github.com/z-x-yang/segment-and-track-anything
공식 코드https://github.com/zafirshi/panovpr
공식 코드https://github.com/zahrag/BRLVBVC
공식 코드A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control
https://github.com/zaidtahirbutt/Situation-Coverage-based-AV-Testing-Framework-in-CARLA
공식 코드https://github.com/zaplm/driverattention
공식 코드Towards Robust Unsupervised Attention Prediction in Autonomous Driving
https://github.com/zarhult/hsda
공식 코드HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentation
https://github.com/zc-zhao/drivemonkey
공식 코드Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving
https://github.com/zehuichen123/autoalignv2
공식 코드AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
https://github.com/zeng-ziyin/LACV_Net
공식 코드https://github.com/zenseact/zod
공식 코드Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
https://github.com/zgojcic/Rigid3DSceneFlow
공식 코드https://github.com/zhang-tao-whu/DVIS
공식 코드https://github.com/zhang-zengjie/code_2023_iecon_shaping_wu
공식 코드Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
https://github.com/zhang-zengjie/intentaware
공식 코드Intention-Aware Control Based on Belief-Space Specifications and Stochastic Expansion
https://github.com/zhanggang001/hednet
공식 코드HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds
https://github.com/zhanggongjie/mapvr
공식 코드Online Map Vectorization for Autonomous Driving: A Rasterization Perspective
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.