18 citations · 48 across the 6 of their papers we have counts for
6 papers · 1 filter
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction
Nikolay Savinov, Lubor Ladicky, Christian Haene +1
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 M…
Matching neural paths: transfer from recognition to correspondence search
Nikolay Savinov, Lubor Ladicky, Marc Pollefeys
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 p…
Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark
Timo Hackel, Nikolay Savinov, Lubor Ladicky +3
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.…
Quad-networks: unsupervised learning to rank for interest point detection
Nikolay Savinov, Akihito Seki, Lubor Ladicky +2
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…
Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint
Nikolay Savinov, Christian Haene, Lubor Ladicky +1
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 penalizati…
Learning the Matching Function
Ľubor Ladický, Christian Häne, Marc Pollefeys
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…