https://github.com/vdigpku/rcbevdet
공식 코드RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection
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/vdigpku/teocc
공식 코드TEOcc: Radar-camera Multi-modal Occupancy Prediction via Temporal Enhancement
https://github.com/velor2012/tdo-dif
공식 코드Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion
https://github.com/victoryxl/replenishmentenv
공식 코드A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management
https://github.com/viktorkare/bevf
공식 코드Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data
https://github.com/vision-sjtu/lightning-nerf
공식 코드Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous Driving
https://github.com/visualaikhu/monowad
공식 코드MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection
https://github.com/vita-epfl/SVGNet
공식 코드https://github.com/vita-epfl/bounding-box-prediction
공식 코드https://github.com/vita-epfl/causalmotion
공식 코드Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
https://github.com/vita-epfl/causalsim2real
공식 코드https://github.com/vita-epfl/cim
공식 코드https://github.com/vita-epfl/detection-attributes-fields
공식 코드https://github.com/vita-epfl/gem
공식 코드https://github.com/vita-epfl/hybrid-feature-fusion
공식 코드Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion
https://github.com/vita-epfl/motion-style-transfer
공식 코드Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting
https://github.com/vita-epfl/pedestrian-transition-dataset
공식 코드Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion
https://github.com/vita-epfl/social-nce
공식 코드Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/vita-epfl/stay-on-track
공식 코드https://github.com/vivianszf/stereo-waterdrop-removal
공식 코드https://github.com/vllm-project/vllm
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/vobecant/DummyNet
공식 코드https://github.com/vpulab/synthmanticlidar
공식 코드SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging
https://github.com/vrund0212/calibrated-losses-for-adversarial-robust-reject-option-classification
공식 코드Towards Calibrated Losses for Adversarial Robust Reject Option Classification
https://github.com/vzyrianov/LidarDM
공식 코드https://github.com/wang-jh18-svm/griffin
공식 코드Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark
https://github.com/wangguojun2018/CenterNet3d
공식 코드https://github.com/wangguojun2018/MVAF-Net
공식 코드https://github.com/wangsen99/FDLNet
공식 코드https://github.com/wangsh0111/iftr
공식 코드IFTR: An Instance-Level Fusion Transformer for Visual Collaborative Perception
https://github.com/wangwenxi-handsome/joint-multipathpp
공식 코드https://github.com/wangyueft/detr3d
공식 코드DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
https://github.com/wangzc0422/c2f-tp
공식 코드C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction
https://github.com/wanifarooq/ncod
공식 코드Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
https://github.com/waterscenes/waterscenes
공식 코드https://github.com/wayveai/driving-with-llms
공식 코드Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.