Akhmedkhan (Ahan) Shabanov

Hi! I am a PhD student at the GrUVi lab of SFU advised by Andrea Tagliasacchi in a beautiful Vancouver. I work on 3D computer vision.

Previously, I was a research engineer (founding team member) at Avaturn (ex. In3D) working with Dmitry Ulyanov on 3D human body and face reconstruction from in-the-wild images. Even before that, I received my BSc and MSc degrees from Moscow Institute of Physics and Technology in applied mathematics and physics.

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News

Publications

BANF: Band-limited Neural Fields for Levels of Detail Reconstruction

BANF: Band-limited Neural Fields for Levels of Detail Reconstruction

Ahan Shabanov, Shrisudhan Govindarajan, Cody Reading, Lily Goli, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi
CVPR 2024

We explore how a simple modification to neural fields enables low-pass filtering, facilitating improved frequency decomposition which is crucial for level-of-detail reconstruction.

Project Page arXiv Code
Lagrangian Hashing for Compressed Neural Field Representations

Lagrangian Hashing for Compressed Neural Field Representations

Shrisudhan Govindarajan, Zeno Sambugaro, Ahan Shabanov, Towaki Takikawa, Weiwei Sun, Daniel Rebain, Nicola Conci, Kwang Moo Yi, Andrea Tagliasacchi
ECCV 2024

A representation for neural fields combining the characteristics of Eulerian grids (i.e.~InstantNGP), with those that employ points equipped with features as a way to represent information (e.g. 3D Gaussian Splatting or PointNeRF).

Project Page arXiv Code
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors

Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors

Ahan Shabanov, Ilya Krotov, Nikolay Chinaev, Vsevolod Poletaev, Sergei Kozlukov, Igor Pasechnik, Bulat Yakupov, Artsiom Sanakoyeu, Vadim Lebedev, Dmitry Ulyanov
3DV, 2020

Denoising iPhone's depth images by using Kinect depth sensor

Project Page arXiv

Other Projects

3D Head Reconstruction

3D Head Reconstruction

2022; US Patent

3D reconstruction of the head and hair from 2D images

Project Page
Unsupervised temporal consistency improvement for microscopy video segmentation with Siamese networks

Unsupervised temporal consistency improvement for microscopy video segmentation with Siamese networks

Ahan Shabanov, Daja Schichler, Constantin Pape, Sara Cuylen-Haering, Anna Kreshuk
2021

We enhance video segmentation by re-training a CNN in a Siamese setup, optimizing both accuracy on labeled images and consistency across unlabeled frames.

BioRxiv Code