Papers with Code 아카이브
Visual Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 31
Visual Localization is the problem of estimating the camera pose of a given image relative to a visual representation of a known scene.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/intelligolabs/Le-RNR-Map
https://github.com/jac99/minkloc3dv2
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
https://github.com/jan-dufek/emily-tracker
Visual Servoing of Unmanned Surface Vehicle from Small Tethered Unmanned Aerial Vehicle
https://github.com/jomariya23156/SuperGlue-for-Visual-Place-Recognition
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/juanjo-cabrera/minkunext
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
https://github.com/kolkir/coarse_loftr_trt
LoFTR: Detector-Free Local Feature Matching with Transformers
https://github.com/kornia/kornia
LoFTR: Detector-Free Local Feature Matching with Transformers
https://github.com/layumi/AICIty-reID-2020
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
https://github.com/liw95/SGLoc
https://github.com/liw95/diffloc
https://github.com/nodarsensor/medium-keypoint-autocal
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/postech-cvlab/scnerf
https://github.com/quan-meng/gnerf
https://github.com/robot-learning-freiburg/DynaFill
Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning
https://github.com/rogerwwww/PCA-GM
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/taowenyin/PatchNetVLAD
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
https://github.com/tzvikif/SuperGlue
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/ucuapps/openglue
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/uditsharma29/flynet-cann
A Hybrid Compact Neural Architecture for Visual Place Recognition
https://github.com/vdvchen/sgmnet
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/verlab/accelerated_features
https://github.com/vinits5/learning3d
Deep Closest Point: Learning Representations for Point Cloud Registration
https://github.com/vislearn/DSACLine
https://github.com/vislearn/dsacstar
https://github.com/vislearn/ngdsac_camreloc
https://github.com/vislearn/ngdsac_horizon
Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses
https://github.com/wtyhub/LPN
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
https://github.com/wzh99/DCP-TF
Deep Closest Point: Learning Representations for Point Cloud Registration
https://github.com/xlearning-scu/2023-iccv-common
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/ybchun/lisa
https://github.com/yolish/c2f-ms-transformer
Learning Multi-Scene Absolute Pose Regression with Transformers
https://github.com/zhuyuhua1/contrib/tree/s2dnet/application/S2DNet-Minimal
S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching
https://github.com/zju3dv/RVL-Dynamic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.