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/biter0088/pc-nerf
공식 코드https://github.com/bitfsd/fsd_algorithm
공식 코드From perception to control: an autonomous driving system for a formula student driverless car
https://github.com/bjork-crypto/visled-querying
공식 코드Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
https://github.com/blackPython/mipae
공식 코드Mutual Information Based Method for Unsupervised Disentanglement of Video Representation
https://github.com/blurryface0814/pcpnet
공식 코드PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction
https://github.com/bmhopkinson/hyslam
공식 코드High-resolution Ecosystem Mapping in Repetitive Environments Using Dual Camera SLAM
https://github.com/bntejn/keras-prioritizer
공식 코드https://github.com/bob-cheng/badpart
공식 코드BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
https://github.com/bob-cheng/cl-fusionattack
공식 코드Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
https://github.com/bogus2000/NOLBO
공식 코드https://github.com/bonbon-rj/instructdriver
공식 코드https://github.com/bonifazstuhr/feamgan
공식 코드https://github.com/borisivanovic/planningawareevaluation
공식 코드Injecting Planning-Awareness into Prediction and Detection Evaluation
https://github.com/boschresearch/deterministic-graph-deep-state-space-models
공식 코드Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
https://github.com/boschresearch/direg
공식 코드Unsupervised Point Cloud Registration with Self-Distillation
https://github.com/boschresearch/issa
공식 코드Intra-Source Style Augmentation for Improved Domain Generalization
https://github.com/boschresearch/local_neural_transformations
공식 코드Detecting Anomalies within Time Series using Local Neural Transformations
https://github.com/boschresearch/meta-adversarial-training
공식 코드https://github.com/boschresearch/nuscenes_knowledge_graph
공식 코드https://github.com/bosszhe/emiff
공식 코드https://github.com/bosszhe/vimi
공식 코드VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection
https://github.com/boyagesmile/dna-depth
공식 코드Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth Estimation
https://github.com/bpucla/lbebm
공식 코드https://github.com/bradyz/task-distillation
공식 코드https://github.com/bravegroup/drive-wm
공식 코드https://github.com/bravegroup/law
공식 코드Enhancing End-to-End Autonomous Driving with Latent World Model
https://github.com/brunapearson/autoUAV-mtrl
공식 코드https://github.com/brunapearson/mtrl-auto-uav
공식 코드https://github.com/bshfang/self-supervised-motion
공식 코드Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
https://github.com/bupt-ai-cz/LLVIP
공식 코드LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
https://github.com/byangderek/CCF
공식 코드https://github.com/byzhaoai/bm2cp
공식 코드BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities
https://github.com/c0ldstudy/pointsecguard
공식 코드On Adversarial Robustness of Point Cloud Semantic Segmentation
https://github.com/c0pep0d/rlfl0w
공식 코드A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows
https://github.com/cainand/refav
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.