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Mateusz Michalkiewicz

5 accepted papers

2026

GIQ: Benchmarking 3D Geometric Reasoning of Vision Foundation Models with Simulated and Real Polyhedra

Mateusz Michalkiewicz, Anekha Sokhal, Tadeusz Michalkiewicz, Piotr Pawlikowski, Mahsa Baktashmotlagh, Varun Jampani +1
ICLR 2026poster

Monocular 3D reconstruction methods and vision-language models (VLMs) demonstrate impressive results on standard benchmarks, yet their true understanding of geometric properties remains unclear. We introduce GIQ, a comprehensive benchmark specifically designed to evaluate the geometric reasoning cap…

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2025

Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models

Mateusz Michalkiewicz, Sheena Bai, Mahsa Baktashmotlagh, Varun Jampani, Guha Balakrishnan
ICCV 2025poster

In this paper, we analyze the viewpoint stability of foundational models - specifically, their sensitivity to changes in viewpoint- and define instability as significant feature variations resulting from minor changes in viewing angle, leading to generalization gaps in 3D reasoning tasks. We investi…

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2023

Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters

Mateusz Michalkiewicz, Masoud Faraki, Xiang Yu, Manmohan Chandraker, Mahsa Baktashmotlagh
ICCV 2023poster

Overfitting to the source domain is a common issue in gradient-based training of deep neural networks. To compensate for the over-parameterized models, numerous regularization techniques have been introduced such as those based on dropout. While these methods achieve significant improvements on clas…

Cited by 5PDF ↗Scholar ↗
2020

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky
ECCV 2020poster

The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. However, recent work has challenged this belief, showing that complex encoder-decoder architectures p…

Cited by 27Source ↗PDF ↗Scholar ↗
2019

Implicit Surface Representations As Layers in Neural Networks

Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack, Mahsa Baktashmotlagh, Anders Eriksson
ICCV 2019poster

Implicit shape representations, such as Level Sets, provide a very elegant formulation for performing computations involving curves and surfaces. However, including implicit representations into canonical Neural Network formulations is far from straightforward. This has consequently restricted exist…

Cited by 304PDF ↗Scholar ↗