activity
20242026
most citedcadrille: Multi-modal CAD Reconstruction with Reinforcement Learning

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV20261 cited

cadrille: Multi-modal CAD Reconstruction with Reinforcement Learning

Maksim Kolodiazhnyi, Denis Tarasov, Dmitrii Zhemchuzhnikov +6

Computer-Aided Design (CAD) plays a central role in engineering and manufacturing, making it possible to create precise and editable 3D models. Using a variety of sensor or user-pr…

cs.CV2025

TUN3D: Towards Real-World Scene Understanding from Unposed Images

Anton Konushin, Nikita Drozdov, Bulat Gabdullin +4

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 spatia…

cs.CV2024

UniDet3D: Multi-dataset Indoor 3D Object Detection

Maksim Kolodiazhnyi, Anna Vorontsova, Matvey Skripkin +2

Growing customer demand for smart solutions in robotics and augmented reality has attracted considerable attention to 3D object detection from point clouds. Yet, existing indoor da…

cs.CV2024

SUPER: Selfie Undistortion and Head Pose Editing with Identity Preservation

Polina Karpikova, Andrei Spiridonov, Anna Vorontsova +4

Self-portraits captured from a short distance might look unnatural or even unattractive due to heavy distortions making facial features malformed, and ill-placed head poses. In thi…

cs.CV2024

FAWN: Floor-And-Walls Normal Regularization for Direct Neural TSDF Reconstruction

Anna Sokolova, Anna Vorontsova, Bulat Gabdullin +1

Leveraging 3D semantics for direct 3D reconstruction has a great potential yet unleashed. For instance, by assuming that walls are vertical, and a floor is planar and horizontal, w…

cs.CV2024

MEDeA: Multi-view Efficient Depth Adjustment

Mikhail Artemyev, Anna Vorontsova, Anna Sokolova +1

The majority of modern single-view depth estimation methods predict relative depth and thus cannot be directly applied in many real-world scenarios, despite impressive performance…