https://github.com/esrschao/edgeregnet
공식 코드EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 4
- 보존된 평가 기록
- 23
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/essoz/distributed-intersection-traffic-coordination-with-lease
공식 코드https://github.com/eth-pbl/cr3dt
공식 코드https://github.com/ethz-asl/hf_net
공식 코드From Coarse to Fine: Robust Hierarchical Localization at Large Scale
https://github.com/ethz-asl/hfnet
공식 코드From Coarse to Fine: Robust Hierarchical Localization at Large Scale
https://github.com/ethz-asl/kalibr
공식 코드https://github.com/ethz-asl/segmap
공식 코드SegMap: Segment-based mapping and localization using data-driven descriptors
https://github.com/etrontech/vfmseg
공식 코드https://github.com/evendrow/somoformer
공식 코드https://github.com/eventbasedvision/sevd
공식 코드SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception
https://github.com/evocargo/lidar-annotation-is-all-you-need
공식 코드https://github.com/explain3d/dam
공식 코드DAM: Diffusion Activation Maximization for 3D Global Explanations
https://github.com/explain3d/lime-3d
공식 코드Surrogate Model-Based Explainability Methods for Point Cloud NNs
https://github.com/explain3d/pointcloudam
공식 코드https://github.com/explainableml/bayescap
공식 코드BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks
https://github.com/explainableml/uncertaintyawarecycleconsistency
공식 코드https://github.com/explosion5/Dolphins
공식 코드DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving
https://github.com/eyalsel/contextual-streaming-perception
공식 코드https://github.com/fabvio/ld-lsi
공식 코드Enhanced free space detection in multiple lanes based on single CNN with scene identification
https://github.com/fairnessresearch/fairness-testing-of-autonomous-driving-systems
공식 코드Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
https://github.com/fangchangma/self-supervised-depth-completion
공식 코드Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
https://github.com/fanyihua0309/friendnet
공식 코드https://github.com/faridghm/meta-sel
공식 코드3D-model ShapeNet Core Classification using Meta-Semantic Learning
https://github.com/feibryantkit/oafuser
공식 코드OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation
https://github.com/feixue94/sfd2
공식 코드https://github.com/feixue94/vrs-nerf
공식 코드VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field
https://github.com/felipecode/coiltraine
공식 코드Exploring the Limitations of Behavior Cloning for Autonomous Driving
https://github.com/felixbmuller/sast
공식 코드Massively Multi-Person 3D Human Motion Forecasting with Scene Context
https://github.com/fengjiang5/knowledge-distillation-from-cylinder3d-to-polarnet
공식 코드Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic Segmentation
https://github.com/fengluodb/lenet
공식 코드LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
https://github.com/fengyi233/SCIPaD
공식 코드SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning
https://github.com/fengyi233/ViPOcc
공식 코드https://github.com/fengzicai/awesomewmad
공식 코드https://github.com/fengzicai/wmad-benchmarks
공식 코드https://github.com/fengziyue/RRT-Parking
공식 코드Model-based Decision Making with Imagination for Autonomous Parking
https://github.com/ffyyytt/coool_2025
공식 코드Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL Challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.