Research outputs
Selected peer-reviewed papers and conference work co-authored by M:3L researchers, with official records, methods, datasets, code, and paper-reported evidence.
Benchmark claims are tied to the associated paper and a named dataset, metric, input setting, and table. Potential applications are kept separate from demonstrated results.
David Aznaurov, Davit Piliposyan, Danila Rukhovich, Sébastien Guenneau
A neural-field and differentiable-FEM framework for designing broadband Rayleigh-wave carpet cloaks within realizable Cauchy materials and mapping the optimized fields to explicit manufacturable microstructures.
Nikita Drozdov, Andrey Lemeshko, Nikita Gavrilov, Anton Konushin, Danila Rukhovich, Maksim Kolodiazhnyi
A universal pipeline for localizing objects in 3D scenes from multi-view images using zero-shot methods, without geometric supervision or object priors.
Andrey Lemeshko, Bulat Gabdullin, Nikita Drozdov, Anton Konushin, Danila Rukhovich, Maksim Kolodiazhnyi
The first training-free 3D object detection framework. It constructs 3D bounding boxes through graph clustering of 2D instance masks and assigns open-vocabulary semantic labels.
Anton Konushin, Nikita Drozdov, Bulat Gabdullin, Alexey Zakharov, Anna Vorontsova, Danila Rukhovich, Maksim Kolodiazhnyi
The first method for joint layout estimation and 3D detection without camera poses or depth supervision, designed to work with point clouds, posed images, or unposed RGB captures.
Maksim Kolodiazhnyi, Denis Tarasov, Dmitrii Zhemchuzhnikov, Alexander Nikulin, Ilya Zisman, Anna Vorontsova, Anton Konushin, Vladislav Kurenkov, Danila Rukhovich
A vision-language model that processes point clouds, multi-view images, and text to generate editable CAD programs. It is the first work to apply online reinforcement learning to CAD reconstruction and achieves a near-zero invalidity ratio.
A dynamic, multi-level assessment using computer-vision analysis of historic satellite imagery over monumental heritage sites of Armenia.