https://bitbucket.org/fafz/stein-icp
공식 코드Stein ICP for Uncertainty Estimation in Point Cloud Matching
Papers with Code 아카이브
Sensor Fusion
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Sensor fusion is the process of combining sensor data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. [Wikipedia]
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/4dvlab/stcrowd
공식 코드STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/ApolloScapeAuto/dataset-api
공식 코드The ApolloScape Open Dataset for Autonomous Driving and its Application
https://github.com/CMVS-Lab/SAC-Residual-Fusion-CARLA
공식 코드Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning
https://github.com/Divadi/MTC_RCNN
공식 코드https://github.com/DroneDetectionThesis/Drone-detection-dataset
공식 코드Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors
https://github.com/GatorSense/MIMRF
공식 코드Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
https://github.com/GuanRunwei/Achelous
공식 코드https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
공식 코드A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
https://github.com/HKUST-Aerial-Robotics/VINS-Mono
공식 코드Online Temporal Calibration for Monocular Visual-Inertial Systems
https://github.com/HannahHaensen/pyfu
공식 코드Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation
https://github.com/ICEORY/PMF
공식 코드EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation
https://github.com/ITSEG-MQ/Sensor-Fusion-Against-VoiceCommand-Attacks
공식 코드Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles
https://github.com/NatiOfir/SuperPixelFusion
공식 코드Multispectral image fusion based on super pixel segmentation
https://github.com/Radar-Camera-Fusion/Awesome-Radar-Camera-Fusion
공식 코드https://github.com/Sachini/Fusion-DHL
공식 코드Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments
https://github.com/TUMFTM/CameraRadarFusionNet
공식 코드A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
https://github.com/TUMFTM/RadarVoxelFusionNet
공식 코드https://github.com/UMich-BipedLab/LiDARTag
공식 코드https://github.com/UMich-BipedLab/extrinsic_lidar_camera_calibration
공식 코드https://github.com/ZhouZijie77/LCPR
공식 코드LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition
https://github.com/aiasd/fused
공식 코드Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems
https://github.com/aicps/hydrafusion
공식 코드https://github.com/aleksandrkim61/EagerMOT
공식 코드https://github.com/alphaplustt/daocc
공식 코드DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction
https://github.com/ansfl/a-kit
공식 코드https://github.com/ansfl/lqg-a-ipoc
공식 코드Inertial-Based LQG Control: A New Look at Inverted Pendulum Stabilization
https://github.com/autonomousvision/transfuser
공식 코드Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
https://github.com/avstack-lab/demo-platform
공식 코드A Modular Platform For Collaborative, Distributed Sensor Fusion
https://github.com/barrybarry-smith/driv3r
공식 코드Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving
https://github.com/bernwang/latte
공식 코드https://github.com/bob-cheng/cl-fusionattack
공식 코드Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
https://github.com/choyingw/GAIS-Net
공식 코드https://github.com/claud1234/clft
공식 코드CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving
https://github.com/claud1234/fcn_transformer_object_segmentation
공식 코드CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving
https://github.com/coreylammie/TBCAS-Towards-Healthcare-and-Biomedical-Applications
공식 코드Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.