activity
20202026
most citedIndependent Component Alignment for Multi-Task Learning

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Unified Sensor Simulation for Autonomous Driving

Nikolay Patakin, Arsenii Shirokov, Anton Konushin +1

In this work, we introduce \textbf{XSIM}, a sensor simulation framework for autonomous driving. XSIM extends 3DGUT splatting with a generalized rolling-shutter modeling tailored fo…

cs.CV2026

Visual Implicit Geometry Transformer for Autonomous Driving

Arsenii Shirokov, Mikhail Kuznetsov, Danila Stepochkin +5

We introduce the Visual Implicit Geometry Transformer (ViGT), an autonomous driving geometric model that estimates continuous 3D occupancy fields from surround-view camera rigs. Vi…

cs.CV2024★ 1 cited

DepthART: Monocular Depth Estimation as Autoregressive Refinement Task

Bulat Gabdullin, Nina Konovalova, Nikolay Patakin +2

Monocular depth estimation has seen significant advances through discriminative approaches, yet their performance remains constrained by the limitations of training datasets. While…

cs.CV2024

A3D: Does Diffusion Dream about 3D Alignment?

Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych +9

We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically…

cs.CV2023★ 1 cited

Independent Component Alignment for Multi-Task Learning

Dmitry Senushkin, Nikolay Patakin, Arseny Kuznetsov +1

In a multi-task learning (MTL) setting, a single model is trained to tackle a diverse set of tasks jointly. Despite rapid progress in the field, MTL remains challenging due to opti…

cs.CV2020

Decoder Modulation for Indoor Depth Completion

Dmitry Senushkin, Mikhail Romanov, Ilia Belikov +2

Depth completion recovers a dense depth map from sensor measurements. Current methods are mostly tailored for very sparse depth measurements from LiDARs in outdoor settings, while…