253 citations · 276 across the 15 of their papers we have counts for
17 papers
CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image
Cuong Le, Bao-Long Tran, Pavlo Melnyk +3
Recovering 3D digital humans from a single 2D image is an ill-posed computer vision problem due to the loss of depth information. Probabilistic 3D human pose estimation compensates…
Gravity-guided Contact Dynamics Estimation from 3D Human Motions
Cuong Le, Urs Waldmann, Bastian Wandt +1
Ground contact forces acting on the human body, are crucial for biomechanics studies or sport performance analysis. Prior methods rely on force plates or pressure mats to collect g…
Benchmark Dataset for Catalysis on 2D MXenes
Pavlo Melnyk, Anmar Karmush, Mårten Wadenbäck +4
Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials. We focus on two-dimensional (2D) T…
LoMa: Local Feature Matching Revisited
David Nordström, Johan Edstedt, Georg Bökman +6
Local feature matching has long been a fundamental component of 3D vision systems such as Structure-from-Motion (SfM), yet progress has lagged behind the rapid advances of modern d…
QuaMo: Quaternion Motions for Vision-based 3D Human Kinematics Capture
Cuong Le, Pavlo Melnyk, Urs Waldmann +2
Vision-based 3D human motion capture from videos remains a challenge in computer vision. Traditional 3D pose estimation approaches often ignore the temporal consistency between fra…
Flow Matching for Probabilistic Monocular 3D Human Pose Estimation
Cuong Le, Pavlo Melnyk, Bastian Wandt +1
Recovering 3D human poses from a monocular camera view is a highly ill-posed problem due to the depth ambiguity. Earlier studies on 3D human pose lifting from 2D often contain inco…
RoMa v2: Harder Better Faster Denser Feature Matching
Johan Edstedt, David Nordström, Yushan Zhang +7
Dense feature matching aims to estimate all correspondences between two images of a 3D scene and has recently been established as the gold standard due to its high accuracy and rob…
Radially Distorted Homographies, Revisited
Mårten Wadenbäck, Marcus Valtonen Örnhag, Johan Edstedt
Homographies are among the most prevalent transformations occurring in geometric computer vision and projective geometry, and homography estimation is consequently a crucial step i…
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
Johan Edstedt, Georg Bökman, Mårten Wadenbäck +1
Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consiste…
RoMa: Robust Dense Feature Matching
Johan Edstedt, Qiyu Sun, Georg Bökman +2
Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences…
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis
Pavlo Melnyk, Andreas Robinson, Michael Felsberg +1
In many practical applications, 3D point cloud analysis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections,…
DKM: Dense Kernelized Feature Matching for Geometry Estimation
Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenbäck +1
Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead…
Steerable 3D Spherical Neurons
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck
Emerging from low-level vision theory, steerable filters found their counterpart in prior work on steerable convolutional neural networks equivariant to rigid transformations. In o…
Trust Your IMU: Consequences of Ignoring the IMU Drift
Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck +2
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. This allows us t…
Efficient Real-Time Radial Distortion Correction for UAVs
Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck +2
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…
Embed Me If You Can: A Geometric Perceptron
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck
Solving geometric tasks involving point clouds by using machine learning is a challenging problem. Standard feed-forward neural networks combine linear or, if the bias parameter is…
Minimal Solvers for Indoor UAV Positioning
Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck +2
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. We focus on cases where additional informat…