Iaro Melekhov

I am a research scientist at Sharper Shape and a postdoc (part-time) at Aalto University in Juho Kannala's Research Group My research interests lie at the intersection of 3D computer vision, computer graphics, and machine learning. I am particularly interested in scene understanding, camera relocalization and mapping, and developing new machine learning methods for 3D computer vision applications.

Previously, I was a research intern at Niantic Labs (London) under the supervision of Prof. Gabriel Brostow and Dr. Daniyar Turmukhambetov. Before this, I spent 6 months in Cambridge, UK working on visual perception algorithms for autonomous cars and having fun at Wayve. I also did a research visit to Marc Pollefeys' Computer Vision and Geometry Group in the Department of Computer Science at ETH Zurich.

Email / CV / Google Scholar / X / Github

Research

My research interests include machine learning for 3D computer vision, generative AI, large-scale visual mapping and localization, end-to-end learning and feature matching with applications in autonomous driving, AR/VR.

News

July 2024: 1 paper accepted to ECCV 2024
June 2024: 1 paper accepted to a CVPR 2024 workshop
January 2024: 1 paper accepted to IJCV
June 2023: I joined Aalto University (Juho Kannala's research group) as a part-time postdoc
June 2022: I joined Sharper Shape as a research scientist
October 2021: 1 paper accepted to 3DV 2021
July 2021: 1 paper accepted to ICCV 2021

Publications

The full list of my papers can be found on Google Scholar. The list of selected projects:

	Differentiable Product Quantization for Memory Efficient Camera Relocalization Zakaria Laskar, Iaroslav Melekhov, Assia Benbihi, Shuzhe Wang, Juho Kannala ECCV, 2024 paper / project page / code We propose a differentiable product quantization layer to address the memory efficiency of the camera relocalization pipeline.
	DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari, Esa Rahtu, Juho Kannala arXiv, 2024 paper / project page / code We extend 3D Gaussian splatting with depth and normal cues to tackle challenging indoor datasets and showcase techniques for efficient mesh extraction.
	ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation Iaroslav Melekhov, Anand Umashankar, Hyeong-Jin Kim, Vladislav Serkov, Dusty Argyle CVPR Workshops, 2024 paper / project page / code We introduce ECLAIR, a diverse and high-fidelity aerial LiDAR dataset for point cloud semantic segmentation.
	Digging Into Self-Supervised Learning of Feature Descriptors Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, Juho Kannala 3DV, 2021 paper / project page / code We show how to use unsupervised learning to optimize a CNN-based local descriptor that is robust to illumination changes and competitve with its fully-supervised counterparts.

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