D2S: Representing sparse descriptors and scene coordinates for visual relocalization
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Updated
May 30, 2024 - Python
D2S: Representing sparse descriptors and scene coordinates for visual relocalization
Official repository for the ICLR 2024 paper "Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition".
[ECCV 2022] Map-free Visual Relocalization: Metric Pose Relative to a Single Image
Este repositorio sintetiza e integra los códigos utilizados para el desarrollo del trabajo de grado titulado: SISMICIDAD E IMPLICACIONES SISMOTECTÓNICAS DE UNA ZONA DEL CENTRO – ORIENTE DE LA CUENCA DEL VALLE MEDIO DEL MAGDALENA (VMM) Y NIDO SÍSMICO DE BUCARAMANGA con énfasis en detección automática de fases sísmicas y relocalización de sismos.
[TRO] Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
Official repository for the AAAI 2024 paper "Deep Homography Estimation for Visual Place Recognition".
Official repository for the CVPR 2024 paper "CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition".
深度学习和三维视觉相关的论文
MixVPR: Feature Mixing for Visual Place Recognition (WACV 2023)
[3DV21] Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision, M. Türkoǧlu et al.
Homography-based loss function for camera pose regression
Code for the CoRL 2021 paper "SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition \& Relocalization"
The framework for visual place recognition in changing enviroments. Matches two sequence of images of arbitrary trajectory overlap.
This is a robotic package for an algorithm for visual teach and repeat
Fast-bag-of-words ROS wrapper for feature-based place detection and relocalization.
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization
A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.
A simple system that can relocalize a robot on a built map is developed in this system. The system is based on LIO-SAM.
This a simple implementation of V-LOAM
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