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Danila Rukhovich
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Imvoxelnet: Image to voxels projection for monocular and multi-view general-purpose 3d object detection
D Rukhovich, A Vorontsova, A Konushin
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
1382022
Fcaf3d: Fully convolutional anchor-free 3d object detection
D Rukhovich, A Vorontsova, A Konushin
European Conference on Computer Vision, 477-493, 2022
912022
IterDet: Iterative Scheme for Object Detection in Crowded Environments
D Rukhovich, K Sofiiuk, D Galeev, O Barinova, A Konushin
Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2021
492021
The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution
DI Rukhovich, PV Koroleva, DD Rukhovich, NV Kalinina
Remote Sensing 13 (1), 155, 2021
282021
Location of bare soil surface and soil line on the RED–NIR spectral plane
PV Koroleva, DI Rukhovich, AD Rukhovich, DD Rukhovich, AL Kulyanitsa, ...
Eurasian soil science 50, 1375-1385, 2017
282017
Maps of averaged spectral deviations from soil lines and their comparison with traditional soil maps
DI Rukhovich, AD Rukhovich, DD Rukhovich, MS Simakova, ...
Eurasian Soil Science 49, 739-756, 2016
262016
The informativeness of coefficients a and b of the soil line for the analysis of remote sensing materials
DI Rukhovich, AD Rukhovich, DD Rukhovich, MS Simakova, ...
Eurasian Soil Science 49, 831-845, 2016
242016
Tr3d: Towards real-time indoor 3d object detection
D Rukhovich, A Vorontsova, A Konushin
2023 IEEE International Conference on Image Processing (ICIP), 281-285, 2023
152023
Информативность коэффициентов a и b линии почв для анализа материалов дистанционного зондирования
ДИ Рухович, АД Рухович, ДД Рухович, МС Симакова, АЛ Куляница, ...
Почвоведение, 903-917, 2016
152016
The application of the piecewise linear approximation to the spectral neighborhood of soil line for the analysis of the quality of normalization of remote sensing materials
AL Kulyanitsa, AD Rukhovich, DD Rukhovich, PV Koroleva, DI Rukhovich, ...
Eurasian Soil Science 50, 387-395, 2017
142017
Построение карт усредненных спектральных отклонений от линии почв и их сравнение с традиционными почвенными картами
ДИ Рухович, АД Рухович, ДД Рухович, МС Симакова, АЛ Куляница, ...
Почвоведение, 794-812, 2016
132016
Местоположение открытой поверхности почвы и линии почвы в спектральном пространстве RED-NIR
ПВ Королева, ДИ Рухович, АД Рухович, ДД Рухович, АЛ Куляница, ...
Почвоведение, 1435-1446, 2017
122017
Применение почвенной линии для построения карт усредненных спектральных отклонений и их почвенная интерпретация
ДИ Рухович, АД Рухович, ДД Рухович, ЕВ Вильчевская, ГА Сулейман, ...
Информация и космос, 125-142, 2015
122015
Top-down beats bottom-up in 3d instance segmentation
M Kolodiazhnyi, A Vorontsova, A Konushin, D Rukhovich
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024
112024
Learning High-Resolution Domain-Specific Representations with a GAN Generator
D Galeev, K Sofiiuk, D Rukhovich, M Romanov, O Barinova, A Konushin
Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2021
112021
Mixmatch domain adaptaion: Prize-winning solution for both tracks of visda 2019 challenge
D Rukhovich, D Galeev
arXiv preprint arXiv:1910.03903, 2019
112019
Применение кусочно-линейной аппроксимации спектральной окрестности линии почв для анализа качества нормализации материалов дистанционного зондирования
АЛ Куляница, АД Рухович, ДД Рухович, ПВ Королева, ДИ Рухович, ...
Почвоведение, 401-410, 2017
112017
Characterization of soil types and subtypes in N-dimensional space of multitemporal (empirical) soil line
PV Koroleva, DI Rukhovich, AD Rukhovich, DD Rukhovich, AL Kulyanitsa, ...
Eurasian soil science 51, 1021-1033, 2018
82018
Application of soil line for creating the maps of averaged spectral deviations and their soil interpretation
DI Rukhovich, AD Rukhovich, DD Rukhovich, EV Vil’chevskaya, ...
Inf. Kosmos 5 (3), 125-142, 2015
82015
Recognition of the bare soil using deep machine learning methods to create maps of arable soil degradation based on the analysis of multi-temporal remote sensing data
DI Rukhovich, PV Koroleva, DD Rukhovich, AD Rukhovich
Remote Sensing 14 (9), 2224, 2022
72022
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Articles 1–20