26 citations · 42 across the 5 of their papers we have counts for
6 papers
Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans
Alexandr Notchenko, Vladislav Ishimtsev, Alexey Artemov +3
We propose Scan2Part, a method to segment individual parts of objects in real-world, noisy indoor RGB-D scans. To this end, we vary the part hierarchies of objects in indoor scenes…
Towards Part-Based Understanding of RGB-D Scans
Alexey Bokhovkin, Vladislav Ishimtsev, Emil Bogomolov +4
Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-gra…
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
Vladislav Ishimtsev, Alexey Bokhovkin, Alexey Artemov +4
Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gam…
Learning Ensembles of Anomaly Detectors on Synthetic Data
D. Smolyakov, N. Sviridenko, V. Ishimtsev +2
The main aim of this work is to develop and implement an automatic anomaly detection algorithm for meteorological time-series. To achieve this goal we develop an approach to constr…
Conformal k-NN Anomaly Detector for Univariate Data Streams
Vladislav Ishimtsev, Ivan Nazarov, Alexander Bernstein +1
Anomalies in time-series data give essential and often actionable information in many applications. In this paper we consider a model-free anomaly detection method for univariate t…
Conformalized density- and distance-based anomaly detection in time-series data
Evgeny Burnaev, Vladislav Ishimtsev
Anomalies (unusual patterns) in time-series data give essential, and often actionable information in critical situations. Examples can be found in such fields as healthcare, intrus…