Search Results for author:
Found 12 papers, 8 papers with code
OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
We bridge this gap by introducing OrienterNet, the first deep neural network that can localize an image with sub-meter accuracy using the same 2D semantic maps that humans use.
SuperGlue: Learning Feature Matching with Graph Neural Networks
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Ranked #2 on
Visual Place Recognition
on Berlin Kudamm
Deep ChArUco: Dark ChArUco Marker Pose Estimation
We evaluate Deep ChArUco in challenging low-light, high-motion, high-blur scenarios and demonstrate that our approach is superior to a traditional OpenCV-based method for ChArUco marker detection and pose estimation.
Self-Improving Visual Odometry
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 network which computes pointwise data associations across images.
SuperPoint: Self-Supervised Interest Point Detection and Description
This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision.
Toward Geometric Deep SLAM
The first network, MagicPoint, operates on single images and extracts salient 2D points.
RoomNet: End-to-End Room Layout Estimation
This paper focuses on the task of room layout estimation from a monocular RGB image.
Deep Cuboid Detection: Beyond 2D Bounding Boxes
We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects).
Deep Image Homography Estimation
We present a deep convolutional neural network for estimating the relative homography between a pair of images.
Ranked #3 on
Homography Estimation
on PDS-COCO
Visualizing Object Detection Features
We introduce algorithms to visualize feature spaces used by object detectors.
Inverting and Visualizing Features for Object Detection
By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.
Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships
The use of context is critical for scene understanding in computer vision, where the recognition of an object is driven by both local appearance and the objects relationship to other elements of the scene (context).
