DefenseSplat: 通过频率感知过滤增强3D高斯点划的鲁棒性
DefenseSplat: Enhancing the Robustness of 3D Gaussian Splatting via Frequency-Aware Filtering
AI总结：
本文提出一种频率感知过滤策略，通过抑制对抗性噪声提升3D高斯点划的鲁棒性，同时保持训练性能。
摘要：
3D高斯点划（3DGS）已发展为一种强大的实时和高保真3D重建方法，但最近研究表明其易受输入视图中的对抗性扰动影响。通过小波变换分析输入图像低频和高频成分中的对抗性扰动行为，本文设计了一种简单有效的频率感知防御策略，通过过滤高频噪声并保留低频内容来重建训练视图。该方法有效抑制对抗性伪影，同时保持原始场景的真实性。值得注意的是，它对干净数据的训练影响较小，实现了鲁棒性和清洁输入性能之间的良好权衡。通过在多个基准上广泛实验，证明了该方法在无干净地面真实监督的情况下显著增强了3DGS的鲁棒性。通过突出和解决3D高斯点划被忽视的漏洞，本文为更鲁棒和安全的3D重建铺平了道路。
英文摘要：
3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time and high-fidelity 3D reconstruction from posed images. However, recent studies reveal its vulnerability to adversarial corruptions in input views, where imperceptible yet consistent perturbations can drastically degrade rendering quality, increase training and rendering time, and inflate memory usage, even leading to server denial-of-service. In our work, to mitigate this issue, we begin by analyzing the distinct behaviors of adversarial perturbations in the low- and high-frequency components of input images using wavelet transforms. Based on this observation, we design a simple yet effective frequency-aware defense strategy that reconstructs training views by filtering high-frequency noise while preserving low-frequency content. This approach effectively suppresses adversarial artifacts while maintaining the authenticity of the original scene. Notably, it does not significantly impair training on clean data, achieving a desirable trade-off between robustness and performance on clean inputs. Through extensive experiments under a wide range of attack intensities on multiple benchmarks, we demonstrate that our method substantially enhances the robustness of 3DGS without access to clean ground-truth supervision. By highlighting and addressing the overlooked vulnerabilities of 3D Gaussian Splatting, our work paves the way for more robust and secure 3D reconstructions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
