https://github.com/jonathsch/lidar-synthesis
공식 코드LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels
Papers with Code 아카이브
Autonomous Vehicles
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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/jonguti13/parameterprotection
공식 코드https://github.com/jonguti13/tensorfi2
공식 코드https://github.com/joonh-kim/etm
공식 코드Continual Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/josemiki/benchmarking_person_re_id
공식 코드https://github.com/joshuadbruton/tsc
공식 코드Translated Skip Connections -- Expanding the Receptive Fields of Fully Convolutional Neural Networks
https://github.com/joy-driven/granp
공식 코드GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction
https://github.com/jsaric/panoptic-swiftnet
공식 코드Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation
https://github.com/jsford64/jiffy-compression
공식 코드Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences
https://github.com/jsgyun/SLCAN
공식 코드https://github.com/juanb09111/finnforest
공식 코드https://github.com/juanb09111/pandepth
공식 코드https://github.com/judithv/minkunext-si
공식 코드https://github.com/julessanchez/3dlabelprop
공식 코드Domain generalization of 3D semantic segmentation in autonomous driving
https://github.com/julessanchez/coarselabel
공식 코드COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets
https://github.com/juliangebele/CARLANE
공식 코드https://github.com/julianolagana/mt3v2
공식 코드Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
https://github.com/julimueller/disparity-sliding-window
공식 코드Disparity Sliding Window: Object Proposals From Disparity Images
https://github.com/jumponthemoon/allweathernet
공식 코드https://github.com/jun-cen/cmdfusion
공식 코드https://github.com/jun-cen/open_world_3d_semantic_segmentation
공식 코드https://github.com/junbao-zhou/track-no-forgetting
공식 코드TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
https://github.com/junda24/afnet
공식 코드Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving
https://github.com/junfanlin/denselight
공식 코드DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback
https://github.com/junfenggaolab/croprowdetection
공식 코드Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
https://github.com/junkunyuan/hap
공식 코드HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
https://github.com/juntang-zhuang/ShelfNet
공식 코드https://github.com/juntang-zhuang/ShelfNet-lw-cityscapes
공식 코드https://github.com/junyu-z/semi-bevseg
공식 코드Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation
https://github.com/junzhang2016/NTU4DRadLM
공식 코드NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
https://github.com/justusren/exploring-adversarial-attacks-on-neural-networks
공식 코드Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
https://github.com/juyebshin/instagram
공식 코드InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning
https://github.com/jwangjie/mixed-traffic-gp-mpc
공식 코드https://github.com/jwfangit/emem-ndt
공식 코드Vehicle Behavior Prediction by Episodic-Memory Implanted NDT
https://github.com/jwfangit/gate-dap
공식 코드https://github.com/jwthi/expert-lasts
공식 코드Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.