arXiv subjects

Oleksii Nasypanyi

Publications and source records attributed to Oleksii Nasypanyi.

3 recordsLinked to original sources

arXiv2026

Seeing Through the Weights: Privacy Leakage in Scene Coordinate Regression

Scene Coordinate Regression (SCR) methods are increasingly adopted for visual localization. In these approaches, the scene is implicitly encoded within a neural network that regresses a 3D world coordinate for each image pixel. Because the scene is represented only through the network parameters and not stored explicitly as images or maps, such methods are often assumed to be privacy-preserving. In this work, we show that this assumption is incorrect in practice. Specifically, we introduce a query-based attack that reconstructs the 3D geometry of the training environment from an SCR model under different levels of model access. To do so, we repeatedly query the model with batches of proxy images unrelated to the target scene to obtain dense pixel-wise 3D coordinates. Reliable points are identified through their stability under small input perturbations and can be further refined in a white-box setting. These stable points are accumulated across independent query batches to recover the scene geometry. From the recovered 3D representation, we also invert the network features to synthesize images from arbitrary viewpoints, revealing additional appearance information. Experiments on indoor and outdoor datasets demonstrate that substantial portions of training environments can be reconstructed with high geometric fidelity. Beyond geometry, we also recover an approximate color appearance, which exposes recognizable layout and potentially sensitive scene elements. This directly contradicts claims in the literature that SCR representations are privacy-preserving by design, and reveals a real risk when such systems are deployed in private or security-critical spaces. The project page is available at https://jaeminch0.github.io/seeing-through-the-weights-privacy-leakage-in-scene-coordinate-regression.

cs.CV↗

arXiv2026

Pixel-Accurate Epipolar Guided Matching

Keypoint matching can be slow and unreliable in challenging conditions such as repetitive textures or wide-baseline views. In such cases, known geometric relations (e.g., the fundamental matrix) can be used to restrict potential correspondences to a narrow epipolar envelope, thereby reducing the search space and improving robustness. These epipolar-guided matching approaches have proved effective in tasks such as SfM; however, most rely on coarse spatial binning, which introduces approximation errors, requires costly post-processing, and may miss valid correspondences. We address these limitations with an exact formulation that performs candidate selection directly in angular space. In our approach, each keypoint is assigned a tolerance circle which, when viewed from the epipole, defines an angular interval. Matching then becomes a 1D angular interval query, solved efficiently in logarithmic time with a segment tree. This guarantees pixel-level tolerance, supports per-keypoint control, and removes unnecessary descriptor comparisons. Extensive evaluation on ETH3D demonstrates noticeable speedups over existing approaches while recovering exact correspondence sets.

cs.CV↗

arXiv2022

Keypoints Tracking via Transformer Networks

In this thesis, we propose a pioneering work on sparse keypoints tracking across images using transformer networks. While deep learning-based keypoints matching have been widely investigated using graph neural networks - and more recently transformer networks, they remain relatively too slow to operate in real-time and are particularly sensitive to the poor repeatability of the keypoints detectors. In order to address these shortcomings, we propose to study the particular case of real-time and robust keypoints tracking. Specifically, we propose a novel architecture which ensures a fast and robust estimation of the keypoints tracking between successive images of a video sequence. Our method takes advantage of a recent breakthrough in computer vision, namely, visual transformer networks. Our method consists of two successive stages, a coarse matching followed by a fine localization of the keypoints' correspondences prediction. Through various experiments, we demonstrate that our approach achieves competitive results and demonstrates high robustness against adverse conditions, such as illumination change, occlusion and viewpoint differences.

cs.CV↗