https://github.com/farrell236/SVRnet
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
Papers with Code 아카이브
Camera Pose Estimation
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Camera pose estimation is a crucial task in computer vision and robotics that involves determining the position and orientation (pose) of a camera relative to a given reference frame. This task is essential for various applications, such as augmented reality, 3D reconstruction, SLAM, and autonomous navigation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fzi-forschungszentrum-informatik/mrf
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
https://github.com/germain-hug/NRE
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation
https://github.com/gogojjh/ESVO_extension
https://github.com/gouthamvgk/SuperGlue_training
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/huggingface/transformers
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/iodncookie/my_loss_monodepth_master
https://github.com/isennkubilay/monodepth2_tf
https://github.com/jasonyzhang/raydiffusion
https://github.com/jmorlana/dran
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
https://github.com/jomariya23156/SuperGlue-for-Visual-Place-Recognition
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/jzwqaq/monodepth_jzw
https://github.com/kaichen-z/rad
Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic Scenes
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/lastbasket/endo-4dgs
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/minghanz/DepthC3D
https://github.com/nail-hnu/esvio_aa
https://github.com/nail-hnu/esvo2
https://github.com/nodarsensor/medium-keypoint-autocal
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/pengwangucla/DeLS-3D
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map
https://github.com/princeton-vl/Princeton365
https://github.com/qrzyang/pseudo-stereo
https://github.com/raunaks13/GeoNet-PyTorch
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
https://github.com/rnlee1998/SRD
https://github.com/robotvisionmu/densemonoslam
https://github.com/rogerwwww/PCA-GM
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/sailor-z/clnet
https://github.com/shichaoy/cuboid_detect
https://github.com/shichaoy/matlab_cuboid_detect
https://github.com/tiev-tongji/quadric_slam
https://github.com/tudelft/filled-disparity-monodepth
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/ut-austin-rpl/forge
Few-View Object Reconstruction with Unknown Categories and Camera Poses
https://github.com/vdvchen/sgmnet
SuperGlue: Learning Feature Matching with Graph Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.