Search Results for author:
Found 11 papers, 9 papers with code
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
To train a descriptor, we maximize the mutual nearest neighbour objective over the keypoints with a separate network.
RoMa: Robust Dense Feature Matching
The aim is to learn a robust model, i. e., a model able to match under challenging real-world changes.
O$n$ Learning Deep O($n$)-Equivariant Hyperspheres
In this paper, we utilize hyperspheres and regular $n$-simplexes and propose an approach to learning deep features equivariant under the transformations of $n$D reflections and rotations, encompassed by the powerful group of O$(n)$.
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis
In our approach, we perform TetraTransform--an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons--and extract deeper O(3)-equivariant features using vector neurons.
DKM: Dense Kernelized Feature Matching for Geometry Estimation
This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation.
Fully Steerable 3D Spherical Neurons
Emerging from low-level vision theory, steerable filters found their counterpart in prior work on steerable convolutional neural networks equivariant to rigid transformations.
Steerable 3D Spherical Neurons
In our work, we propose a steerable feed-forward learning-based approach that consists of neurons with spherical decision surfaces and operates on point clouds.
Trust Your IMU: Consequences of Ignoring the IMU Drift
In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals.
Efficient Real-Time Radial Distortion Correction for UAVs
In this paper we present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU), that runs in real-time.
Embed Me If You Can: A Geometric Perceptron
Our extension of the MLHP model, the multilayer geometric perceptron (MLGP), and its respective layer units, i. e., geometric neurons, are consistent with the 3D geometry and provide a geometric handle of the learned coefficients.
Minimal Solvers for Indoor UAV Positioning
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation.
