activity
20202026
most citedFeature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding

2 citations · 2 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2026

AgentSTAR: Agentic Shape Tracking and Reconstruction from Monocular Videos

Kirill Mazur, Nikita Karaev, Matthew Chang +2

In this work, we present a method for shape reconstruction and tracking from video via agentic analysis-by-synthesis. Unlike prior methods which first estimate dense pixel correspo…

cs.CV2026

Triangle Splatting SLAM

Nicholas Fry, Eric Dexheimer, Kirill Mazur +2

We present a dense RGB-D SLAM system using differentiable triangles as the 3D map representation. While 3D Gaussian Splatting has emerged as the leading method for novel-view synth…

cs.CV2025

KV-Tracker: Real-Time Pose Tracking with Transformers

Marwan Taher, Ignacio Alzugaray, Kirill Mazur +2

Multi-view 3D geometry networks offer a powerful prior but are prohibitively slow for real-time applications. We propose a novel way to adapt them for online use, enabling real-tim…

cs.CV2025

4D Primitive-Mâché: Glueing Primitives for Persistent 4D Scene Reconstruction

Kirill Mazur, Marwan Taher, Andrew J. Davison

We present a dynamic reconstruction system that receives a casual monocular RGB video as input, and outputs a complete and persistent reconstruction of the scene. In other words, w…

cs.CV2023

SuperPrimitive: Scene Reconstruction at a Primitive Level

Kirill Mazur, Gwangbin Bae, Andrew J. Davison

Joint camera pose and dense geometry estimation from a set of images or a monocular video remains a challenging problem due to its computational complexity and inherent visual ambi…

cs.CV2022★ 2 cited

Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding

Kirill Mazur, Edgar Sucar, Andrew J. Davison

General scene understanding for robotics requires flexible semantic representation, so that novel objects and structures which may not have been known at training time can be ident…

cs.CV2021

Point-Based Modeling of Human Clothing

Ilya Zakharkin, Kirill Mazur, Artur Grigorev +1

We propose a new approach to human clothing modeling based on point clouds. Within this approach, we learn a deep model that can predict point clouds of various outfits, for variou…

cs.CV2020

Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks

Kirill Mazur, Victor Lempitsky

We present a new versatile building block for deep point cloud processing architectures that is equally suited for diverse tasks. This building block combines the ideas of spatial…