Search Results for author:
Found 26 papers, 6 papers with code
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space.
Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation.
Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks
Image convolutions have been a cornerstone of a great number of deep learning advances in computer vision.
A Simple and Scalable Shape Representation for 3D Reconstruction
Deep learning applied to the reconstruction of 3D shapes has seen growing interest.
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization.
Implicitly Defined Layers in Neural Networks
In conventional formulations of multilayer feedforward neural networks, the individual layers are customarily defined by explicit functions.
Visual SLAM: Why Bundle Adjust?
Bundle adjustment plays a vital role in feature-based monocular SLAM.
SASSE: Scalable and Adaptable 6-DOF Pose Estimation
In this paper, we present a novel 6-DOF localization system that for the first time simultaneously achieves all the three characteristics: significantly sub-linear storage growth, agnosticism to image descriptors, and customizability to available storage and computational resources.
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
Star Tracking using an Event Camera
Star trackers are primarily optical devices that are used to estimate the attitude of a spacecraft by recognising and tracking star patterns.
A Binary Optimization Approach for Constrained K-Means Clustering
This approach allows us to solve constrained K-Means where multiple types of constraints can be simultaneously enforced.
Learning Free-Form Deformations for 3D Object Reconstruction
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge.
Maximum Consensus Parameter Estimation by Reweighted $\ell_1$ Methods
Robust parameter estimation in computer vision is frequently accomplished by solving the maximum consensus (MaxCon) problem.
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks.
Deterministic Approximate Methods for Maximum Consensus Robust Fitting
Further, our approach is naturally applicable to estimation problems with geometric residuals
Compact Model Representation for 3D Reconstruction
3D reconstruction from 2D images is a central problem in computer vision.
Rotation Averaging and Strong Duality
In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications.
Richardson-Lucy Deblurring for Moving Light Field Cameras
The method deals correctly with blur caused by 6-degree-of-freedom camera motion in complex 3-D scenes, without performing depth estimation.
Guaranteed Outlier Removal With Mixed Integer Linear Programs
Towards the goal of solving maximum consensus exactly, we present guaranteed outlier removal as a technique to reduce the runtime of exact algorithms.
A Consensus-Based Framework for Distributed Bundle Adjustment
In this paper we study large-scale optimization problems in multi-view geometry, in particular the Bundle Adjustment problem.
Non-linear Dimensionality Regularizer for Solving Inverse Problems
Additionally, we evaluate our method on the challenging problem of Non-Rigid Structure from Motion and our approach delivers promising results on CMU mocap dataset despite the presence of significant occlusions and noise.
The k-Support Norm and Convex Envelopes of Cardinality and Rank
Sparsity, or cardinality, as a tool for feature selection is extremely common in a vast number of current computer vision applications.
Efficient Globally Optimal Consensus Maximisation With Tree Search
We aim to change this state of affairs by proposing a very efficient algorithm for global maximisation of consensus.
Pseudoconvex Proximal Splitting for L-infty Problems in Multiview Geometry
In this paper we study optimization methods for minimizing large-scale pseudoconvex L_infinity problems in multiview geometry.
Fast Convolutional Sparse Coding
Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks.
Visualizing Sentiment Analysis on a User Forum
Sentiment analysis, or opinion mining, is the process of extracting sentiment from documents or sentences, where the expressed sentiment is typically categorized as positive, negative, or neutral.
