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Nikolay Patakin

7 accepted papers

2026

Visual Implicit Geometry Transformer for Autonomous Driving

IJCAI 2026

We introduce the Visual Implicit Geometry Transformer (ViGT), an autonomous driving geometric model that estimates continuous 3D occupancy fields from surround-view camera rigs. ViGT represents a step towards foundational geometric models for autonomous driving, prioritizing scalability, architectur

Cited by 0Scholar
2025

A3D: Does Diffusion Dream about 3D Alignment?

ICLR 2025poster

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 corresponding parts aligned across them. Recent methods based on Score Distillation have succeeded in distilling the kno…

Cited by 0SourcePDFScholar
2025

DepthART: Monocular Depth Estimation as Autoregressive Refinement Task

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 0SourcePDFScholar
2023

Independent Component Alignment for Multi-Task Learning

CVPR 2023poster

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 optimization issues such as conflicting and dominating gradients. In this work, we propose using a condition number of a lin…

2022

Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures With Uncalibrated Stereo Data

CVPR 2022poster

Nowadays, robotics, AR, and 3D modeling applications attract considerable attention to single-view depth estimation (SVDE) as it allows estimating scene geometry from a single RGB image. Recent works have demonstrated that the accuracy of an SVDE method hugely depends on the diversity and volume of…

Cited by 7PDFScholar
2021

Decoder Modulation for Indoor Depth Completion

IROS 2021poster

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 for indoor scenes Time-of-Flight (ToF) or structured light sensors are mostly used. These sensors provide semi-dense map…

Cited by 52SourceScholar