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/georghess/neurad-studio
공식 코드https://github.com/getterupper/PreWorld
공식 코드Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving
https://github.com/ghimiremukesh/autonomous-driving
공식 코드Lane Change Decision-Making through Deep Reinforcement Learning
https://github.com/ghostish/objectcentricocccompletion
공식 코드Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection
https://github.com/ghostish/open3dsot
공식 코드Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
https://github.com/giandbt/synboost
공식 코드https://github.com/giantseaweed/rocas
공식 코드ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation
https://github.com/gigaai-research/general-world-models-survey
공식 코드Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond
https://github.com/gist-ailab/mart
공식 코드MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction
https://github.com/git-disl/EVA
공식 코드Parallel Detection for Efficient Video Analytics at the Edge
https://github.com/git-disl/TOG
공식 코드TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
https://github.com/gitouni/camer-lidar-calib-surrogate-diffusion
공식 코드Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion
https://github.com/gkahn13/LaND
공식 코드https://github.com/gktejus/DMF-Synch
공식 코드https://github.com/gmum/make-interval-bound-propagation-great-again
공식 코드https://github.com/gnardari/urquhart
공식 코드https://github.com/gnns4hri/sngnnv2
공식 코드A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation
https://github.com/gogojjh/cobra
공식 코드Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments
https://github.com/goodarzmehr/simbev
공식 코드SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset
https://github.com/google-research/causal-agents
공식 코드CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships
https://github.com/google/next-prediction
공식 코드Peeking into the Future: Predicting Future Person Activities and Locations in Videos
https://github.com/gorilla-lab-scut/vista
공식 코드VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention
https://github.com/gorkaydemir/FTGN
공식 코드https://github.com/gqding/PedestrianIQA
공식 코드Robustness Analysis of Pedestrian Detectors for Surveillance
https://github.com/gradslam/gradslam
공식 코드https://github.com/grandzxw/triplemixer
공식 코드TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather
https://github.com/great-whu/dba-fusion
공식 코드https://github.com/gt-arc/apr-transformer
공식 코드https://github.com/gtzly/FADNet
공식 코드Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation
https://github.com/guangjinpan/lwlm
공식 코드Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks
https://github.com/guangmingw/DOPlearning
공식 코드Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry
https://github.com/guanh01/wot
공식 코드https://github.com/guanrunwei/asy-vrnet
공식 코드https://github.com/guanrunwei/rd_map_temporal_spatial_denoising_autoencoder
공식 코드https://github.com/gugarosa/synthetic_rbms
공식 코드Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines
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