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20162021
most citedToward Geometric Deep SLAM

45 citations · 45 across the 2 of their papers we have counts for

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6 papers · 1 filter

cs.CV2021

ODAM: Object Detection, Association, and Mapping using Posed RGB Video

Kejie Li, Daniel DeTone, Steven Chen +6

Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics.…

cs.CV2019

SuperGlue: Learning Feature Matching with Graph Neural Networks

Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz +1

This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are est…

cs.CV2018

Self-Improving Visual Odometry

Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a ne…

cs.CV2018

Deep ChArUco: Dark ChArUco Marker Pose Estimation

Danying Hu, Daniel DeTone, Vikram Chauhan +2

ChArUco boards are used for camera calibration, monocular pose estimation, and pose verification in both robotics and augmented reality. Such fiducials are detectable via tradition…

cs.CV201745 cited

Toward Geometric Deep SLAM

Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The e…

cs.CV2016

Deep Image Homography Estimation

Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale…