Search Results for author:
Found 14 papers, 9 papers with code
XFeat: Accelerated Features for Lightweight Image Matching
Besides, our model is designed to offer the choice of matching at the sparse or semi-dense levels, each of which may be more suitable for different downstream applications, such as visual navigation and augmented reality.
Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization
Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects.
Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images
In this paper, we propose an approach to address the problem of 3D reconstruction of scenes from a single image captured by a light-field camera equipped with a rolling shutter sensor.
Improving the matching of deformable objects by learning to detect keypoints
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence.
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval.
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence.
A Practical Calibration Method for RGB Micro-Grid Polarimetric Cameras
In this paper, we propose a new method to overcome the need for complex optical systems to efficiently calibrate these cameras.
Learning Geodesic-Aware Local Features from RGB-D Images
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfaces.
Extracting Deformation-Aware Local Features by Learning to Deform
Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations.
Creating and Reenacting Controllable 3D Humans with Differentiable Rendering
It also infers texture appearance with a convolutional network in the texture domain, which is trained in an adversarial regime to reconstruct human texture from rendered images of actors in different poses.
A Shape-Aware Retargeting Approach to Transfer Human Motion and Appearance in Monocular Videos
Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision.
Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles.
Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
The formulation is designed to identify and to disregard dynamic objects in order to obtain a medium-term invariant map representation.
Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints
Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints.
