Search Results for author:
Found 11 papers, 8 papers with code
AffineGlue: Joint Matching and Robust Estimation
Moreover, we derive a new minimal solver for homography estimation, requiring only a single affine correspondence (AC) and a gravity prior.
LightGlue: Local Feature Matching at Light Speed
We introduce LightGlue, a deep neural network that learns to match local features across images.
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving.
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.
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes.
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement
Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction.
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms.
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
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Deep learning has enabled impressive progress in the accuracy of semantic segmentation.
Ranked #13 on
Anomaly Detection
on Fishyscapes L&F
(using extra training data)
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization.
Ranked #3 on
Visual Place Recognition
on Berlin Kudamm
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating in large and changing environments.
