用4D高斯模型实现3D场景中任意视角的实例级变化检测。
Gaussian Difference: Find Any Change Instance in 3D Scenes
- 用4D高斯嵌入多帧图像，生成可渲染的统一序列。
- 通过实例ID比对与变化图分类，实现跨视角变化定位。
- 在光照剧烈变化下仍保持高精度，适合真实场景应用。
3D场景中的实例级变化检测在无标注图像对、相机位姿不一致或光照不均匀的真实环境中面临巨大挑战。本文提出一种新方法，利用4D高斯将多张图像嵌入高斯分布，生成两组一致的图像序列。对每张图像进行实例分割并分配唯一标识，通过标识对比实现高效变化检测。同时，结合变化图与分类编码，将4D高斯分类为已变或未变，从而可从任意视角渲染完整的变化图。在多个实例级变化检测数据集上的大量实验表明，该方法显著优于C-NERF和CYWS-3D等先进方法，尤其在光照剧烈变化场景中表现突出。本方法提升了检测精度、对光照变化的鲁棒性及处理效率，推动了3D变化检测领域发展。
原文摘要 · Abstract (English)
Instance-level change detection in 3D scenes presents significant challenges, particularly in uncontrolled environments lacking labeled image pairs, consistent camera poses, or uniform lighting conditions. This paper addresses these challenges by introducing a novel approach for detecting changes in real-world scenarios. Our method leverages 4D Gaussians to embed multiple images into Gaussian distributions, enabling the rendering of two coherent image sequences. We segment each image and assign unique identifiers to instances, facilitating efficient change detection through ID comparison. Additionally, we utilize change maps and classification encodings to categorize 4D Gaussians as changed or unchanged, allowing for the rendering of comprehensive change maps from any viewpoint. Extensive experiments across various instance-level change detection datasets demonstrate that our method significantly outperforms state-of-the-art approaches like C-NERF and CYWS-3D, especially in scenarios with substantial lighting variations. Our approach offers improved detection accuracy, robustness to lighting changes, and efficient processing times, advancing the field of 3D change detection.
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