Gaussian Difference: Find Any Change Instance in 3D Scenes

Abstract

Instance-level change detection in 3D scenes presents significant challenges, particularly under uncontrolled conditions without labeled image pairs, varying camera poses, or restricted lighting. This paper addresses this challenge by developing a novel approach to detect changes in real-world scenarios. Leveraging 4D Gaussians to embed multiple images into 3D Gaussian distributions, our method enables the rendering of two coherent image sequences. By segmenting each image and assigning a unique identifier to each instance, we can efficiently identify changed instances through ID comparison. Additionally, we utilize change maps and classification encodings to categorize the 4D Gaussians as changed or unchanged, allowing for the rendering of a comprehensive change map from any view direction. Through extensive experiments on various instance-level change detection datasets, our method demonstrates significant improvements in detection accuracy over state-of-the-art methods like C-NERF and CYWS-3D, particularly in scenarios with large lighting variations.