1 citations · 1 across the 9 of their papers we have counts for
9 papers
CADENA: Stepwise CAD Reverse Engineering
Soslan Kabisov, Gennadiy Savrasov, Maksim Elistratov +9
Computer-Aided Design (CAD) underpins modern engineering, yet converting existing shapes into editable models still demands substantial expert effort. Most AI systems emit the enti…
CADReasoner: Iterative Program Editing for CAD Reverse Engineering
Soslan Kabisov, Vsevolod Kirichuk, Andrey Volkov +5
Computer-Aided Design (CAD) powers modern engineering, yet producing high-quality parts still demands substantial expert effort. Many AI systems tackle CAD reverse engineering, but…
CADEvolve: Creating Realistic CAD via Program Evolution
Maksim Elistratov, Marina Barannikov, Gregory Ivanov +4
Computer-Aided Design (CAD) delivers rapid, editable modeling for engineering and manufacturing. Recent AI progress now makes full automation feasible for various CAD tasks. Howeve…
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…
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…
Z3D: Zero-Shot 3D Visual Grounding from Images
Nikita Drozdov, Andrey Lemeshko, Nikita Gavrilov +3
3D visual grounding (3DVG) aims to localize objects in a 3D scene based on natural language queries. In this work, we explore zero-shot 3DVG from multi-view images alone, without r…
Zoo3D: Zero-Shot 3D Object Detection at Scene Level
Andrey Lemeshko, Bulat Gabdullin, Nikita Drozdov +3
3D object detection is fundamental for spatial understanding. Real-world environments demand models capable of recognizing diverse, previously unseen objects, which remains a major…
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…
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…