Nikolay Patakin

About

Nikolay Patakin is a rising researcher in computer vision, with a focus on single-view depth estimation (SVDE) and 3D scene understanding. His work addresses critical challenges in enabling machines to perceive geometry from a single RGB image—a capability essential for robotics, augmented reality, and 3D modeling. Patakin’s most cited paper, “Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data” (2022, 5 citations), introduces a novel training paradigm that leverages uncalibrated stereo data from diverse datasets without requiring ground-truth depth. This approach preserves geometric consistency while scaling to mixed data sources, overcoming a key bottleneck in SVDE: the reliance on expensive, calibrated datasets. By proposing a single-stage framework that jointly handles domain diversity and geometric fidelity, Patakin’s work advances practical depth estimation in real-world, uncontrolled environments. Though early in his career, his contributions are already shaping how researchers think about data-efficient, geometry-aware learning for 3D perception. His research promises to make depth estimation more robust and accessible for downstream applications, marking him as a promising voice in the field.

Research Focus

Key Achievements

Top Papers

1. 1
   Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data

   5 citations · 2022

Key Collaborators

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