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.
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Publications
The full list of my papers can be found on Google Scholar. The list of selected projects:
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Differentiable Product Quantization for Memory Efficient Camera Relocalization
Zakaria Laskar*,
Iaroslav Melekhov*,
Assia Benbihi,
Shuzhe Wang,
Juho Kannala
ECCV, 2024
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We propose a differentiable product quantization layer to address the memory efficiency of the camera relocalization pipeline.
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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
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We extend 3D Gaussian splatting with depth and normal cues to tackle challenging indoor datasets and showcase techniques for efficient mesh extraction.
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ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation
Iaroslav Melekhov*,
Anand Umashankar*,
Hyeong-Jin Kim,
Vladislav Serkov,
Dusty Argyle
CVPR Workshops, 2024
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We introduce ECLAIR, a diverse and high-fidelity aerial LiDAR dataset for point cloud semantic segmentation.
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Digging Into Self-Supervised Learning of Feature Descriptors
Iaroslav Melekhov*,
Zakaria Laskar*,
Xiaotian Li,
Shuzhe Wang,
Juho Kannala
3DV, 2021
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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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