no code implementations • CVPR 2015 • Nikolay Savinov, Lubor Ladicky, Christian Haene, Marc Pollefeys
The depth and semantic information is incorporated as a unary potential, smoothed by a pairwise regularizer.
no code implementations • ICCV 2017 • Lubor Ladicky, Olivier Saurer, SoHyeon Jeong, Fabio Maninchedda, Marc Pollefeys
Surface reconstruction from a point cloud is a standard subproblem in many algorithms for dense 3D reconstruction from RGB images or depth maps.
no code implementations • NeurIPS 2017 • Nikolay Savinov, Lubor Ladicky, Marc Pollefeys
We propose to use a hierarchical semantic representation of the objects, coming from a convolutional neural network, to solve this ambiguity.
1 code implementation • 12 Apr 2017 • Timo Hackel, Nikolay Savinov, Lubor Ladicky, Jan D. Wegner, Konrad Schindler, Marc Pollefeys
With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks.
no code implementations • CVPR 2017 • Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, Marc Pollefeys
In this paper, we ask a fundamental question: can we learn such detectors from scratch?
1 code implementation • CVPR 2016 • Nikolay Savinov, Christian Haene, Lubor Ladicky, Marc Pollefeys
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.
no code implementations • CVPR 2015 • Christian Hane, Lubor Ladicky, Marc Pollefeys
In this work we make use of recent advances in data driven classification to improve standard approaches for binocular stereo matching and single view depth estimation.
no code implementations • CVPR 2014 • Lubor Ladicky, Jianbo Shi, Marc Pollefeys
The limitations of current state-of-the-art methods for single-view depth estimation and semantic segmentations are closely tied to the property of perspective geometry, that the perceived size of the objects scales inversely with the distance.
no code implementations • CVPR 2013 • Lubor Ladicky, Philip H. S. Torr, Andrew Zisserman
Our goal is to detect humans and estimate their 2D pose in single images.
no code implementations • NeurIPS 2011 • Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari
It provides a set of anchor points which form a local coordinate system, such that each data point on the manifold can be approximated by a linear combination of its anchor points, and the linear weights become the local coordinate coding.
no code implementations • 11 Sep 2011 • Srikumar Ramalingam, Chris Russell, Lubor Ladicky, Philip H. S. Torr
E +n^4 {\log}^{O(1)} n)$ where $E$ is the time required to evaluate the function and $n$ is the number of variables \cite{Lee2015}.