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/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/Cascade-LD
공식 코드https://github.com/fabvio/TuSimple-lane-classes
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/facebookresearch/detectron
공식 코드https://github.com/facebookresearch/habitat-api
공식 코드DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/facebookresearch/pyrobot
공식 코드PyRobot: An Open-source Robotics Framework for Research and Benchmarking
https://github.com/facebookresearch/self_defeating_improvements
공식 코드Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
https://github.com/facebookresearch/vissl
공식 코드Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
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/fangchangma/sparse-to-dense
공식 코드Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/fangshiyuu/codrivingllm
공식 코드https://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/ferdiko/vetl
공식 코드https://github.com/ffyyytt/coool_2025
공식 코드Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL Challenge
https://github.com/fh2019ustc/polysnake
공식 코드Recurrent Generic Contour-based Instance Segmentation with Progressive Learning
https://github.com/fidler-lab/social-driving
공식 코드https://github.com/filangelos/social_rl
공식 코드https://github.com/filipcano/safety-shields-delayed
공식 코드https://github.com/fishmarch/slam_map_compression
공식 코드Efficient Map Sparsification Based on 2D and 3D Discretized Grids
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