Bulat Gabdullin — accepted AI-conference papers

2026

TUN3D: Towards Real-World Scene Understanding from Unposed Images

Anton Konushin, Nikita Drozdov, Bulat Gabdullin, Alexey Zakharov, Anna Vorontsova, Danila Rukhovich +1

ICRA 2026poster

Layout estimation and 3D object detection are two fundamental tasks in indoor scene understanding. When combined, they enable the creation of a compact yet semantically rich spatial representation of a scene. Existing approaches typically rely on point cloud input, which poses a major limitation sin…

Cited by 0 Source ↗PDF ↗Scholar ↗

2026

Zoo3D: Zero-Shot 3D Object Detection at Scene Level

Andrey Lemeshko, Bulat Gabdullin, Nikita Drozdov, Anton Konushin, Danila Rukhovich, Maksim Kolodiazhnyi

CVPR 2026

3D object detection is fundamental for spatial understanding. Real-world environments demand models capable of recognizing diverse, previously unseen objects, which remains a major limitation of closed-set methods. Existing open-vocabulary 3D detectors relax annotation requirements but still depend

Cited by 0 Source ↗code ↗Scholar ↗

2025

DepthART: Monocular Depth Estimation as Autoregressive Refinement Task

Bulat Gabdullin, Nina Konovalova, Nikolay Patakin, Dmitry Senushkin, Anton Konushin

IJCAI 2025

Monocular depth estimation has seen significant advances through discriminative approaches, yet their performance remains constrained by the limitations of training datasets. While generative approaches have addressed this challenge by leveraging priors from internet-scale datasets, with recent stud

Cited by 0 Source ↗PDF ↗Scholar ↗