Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Ladický, Ľubor"'
Publikováno v:
Lecture Notes in Computer Science, vol 13563. Springer, Cham, 2022
Predictive variability due to data ambiguities has typically been addressed via construction of dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest. These approaches req
Externí odkaz:
http://arxiv.org/abs/2308.01731
Dense semantic 3D reconstruction is typically formulated as a discrete or continuous problem over label assignments in a voxel grid, combining semantic and depth likelihoods in a Markov Random Field framework. The depth and semantic information is in
Externí odkaz:
http://arxiv.org/abs/1906.10491
Many machine learning tasks require finding per-part correspondences between objects. In this work we focus on low-level correspondences - a highly ambiguous matching problem. We propose to use a hierarchical semantic representation of the objects, c
Externí odkaz:
http://arxiv.org/abs/1705.08272
Autor:
Hackel, Timo, Savinov, Nikolay, Ladicky, Lubor, Wegner, Jan D., Schindler, Konrad, Pollefeys, Marc
This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convo
Externí odkaz:
http://arxiv.org/abs/1704.03847
Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given domain. Since t
Externí odkaz:
http://arxiv.org/abs/1611.07571
We propose an approach for dense semantic 3D reconstruction which uses a data term that is defined as potentials over viewing rays, combined with continuous surface area penalization. Our formulation is a convex relaxation which we augment with a cru
Externí odkaz:
http://arxiv.org/abs/1604.02885
The matching function for the problem of stereo reconstruction or optical flow has been traditionally designed as a function of the distance between the features describing matched pixels. This approach works under assumption, that the appearance of
Externí odkaz:
http://arxiv.org/abs/1502.00652
Submodular function minimization is a key problem in a wide variety of applications in machine learning, economics, game theory, computer vision, and many others. The general solver has a complexity of $O(n^3 \log^2 n . E +n^4 {\log}^{O(1)} n)$ where
Externí odkaz:
http://arxiv.org/abs/1109.2304
Autor:
Ladický, Ľubor1 lubor@robots.ox.ac.uk, Russell, Chris2 chrisr@eecs.qmul.ac.uk, Kohli, Pushmeet3 pkohli@microsoft.com, Torr, Philip4 philiptorr@brookes.ac.uk
Publikováno v:
International Journal of Computer Vision. Jun2013, Vol. 103 Issue 2, p213-225. 13p. 2 Color Photographs, 1 Diagram, 3 Charts.
