Kaleidoscopic Background Attack: Disrupting Pose Estimation with Multi-Fold Radial Symmetry Textures

Abstract

Camera pose estimation is a fundamental computer vision task that is essential for applications like visual localization and multi-view stereo reconstruction. In the object-centric scenarios with sparse inputs, the accuracy of pose estimation can be significantly influenced by background textures that occupy major portions of the images across different viewpoints. In light of this, we introduce the Kaleidoscopic Background Attack (KBA), which uses identical segments to form discs with multi-fold radial symmetry. These discs maintain high similarity across different viewpoints, enabling effective attacks on pose estimation models even with natural texture segments. Additionally, a projected orientation consistency loss is proposed to optimize the kaleidoscopic segments, leading to significant enhancement in the attack effectiveness. Experimental results show that optimized adversarial kaleidoscopic backgrounds can effectively attack various camera pose estimation models.

Method

In summary, our contributions are as follows:

Inspired by the prevalent symmetry in nature, we propose a method to construct adversarial kaleidoscopic background with multi-fold radial symmetry in object-centric scenes to effectively disrupt camera pose estimation.
We optimize the kaleidoscopic background using orientation consistency loss to significantly enhance the attack effectiveness in both the digital and physical worlds.
To the best of our knowledge, we are the first to utilize background textures as adversarial examples to attack sparse-view camera pose estimation models. Our work introduces a method for constructing challenging samples, which can facilitate improvement in both the performance and robustness of these models in the future.

Experiments

Experimental results of attacking various camera pose estimation models with different background discs in the physical world. Each cell contains two values: the larger value represents the mean of the metric across all samples, while the smaller value indicates the standard deviation of the metric across different object categories. Bold values indicate the best performance of adversarial attacks.

Visualization of experimental results for discs with varying backgrounds across different camera pose estimation models in the physical world. The color of the image borders corresponds to the color of the associated pose pyramid.

(a) Naturally symmetrical textures found in nature (denoted as N-1,2,3) and unoptimized radially symmetrical textures derived from them (denoted as KN-1,2,3). (b) Textures optimized using different combinations of loss functions and background patterns.

Camera pose estimation results on DUSt3R with different backgrounds using the DT1 5 images setting. Bold values indicate the best performance among adversarial attacks.

BibTeX

@inproceedings{Ding2025KBA, title = {Kaleidoscopic Background Attack: Disrupting Pose Estimation with Multi-Fold Radial Symmetry Textures}, author = {Ding, Xinlong and Yu, Hongwei and Li, Jiawei and Li, Feifan and Shang, Yu and Zou, Bochao and Ma, Huimin and Chen, Jiansheng}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, year = {2025}, pages = {to appear} }