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.

Most existing change detection (CD) methods focus on spatial domain modeling, while ignore the rich information of frequency domain. In this paper, we enhance the feature representative ability with spatial and frequency feature cross enhancement. Specifically, we propose a Change Feature Extract Module (CFEM) to obtain high-quality change features from the bi-temporal images. These features are then merged together and refined through three parallel branches: the Local branch uses multiscale max-pooling operations to generate multiscale feature; the Wavelet Transform Decomposer (WTD) branch decomposes feature into low-frequency and high-frequency signals with Haar wavelet transform; the Global branch adopts Mamba to capture the long-range dependencies. To bridge the semantic gap between frequency and spatial features, we design Dual-Representation Aggregation Module (DRAM) to promote the combination of features from different representation domains in a flow-based manner and dual cross attention. Extensive experiments demonstrate our method outperforms 11 SOTA CD methods on three remote sensing CD datasets.

In this work, we aim to detect the changes caused by object variations in a scene represented by the neural radiance fields (NeRFs). Given an arbitrary view and two sets of scene images captured at different timestamps, we can predict scene changes in that view, which has significant potential applications in scene monitoring and measuring. We conducted preliminary studies and found that such an exciting task cannot be easily achieved by utilizing existing NeRFs and 2D change detection (CD) methods with many false or missing detections. The main reason is that the 2D CD is based on the pixel appearance difference between spatial-aligned image pairs and neglects the stereo information in the NeRF. To address the limitations, we propose the C-NeRF to represent scene changes as directional consistency difference-based NeRF, which mainly contains three modules. We first build two aligned NeRFs from pre-change and post-change scenes. Then, we identify the change points based on the direction-consistent constraint; that is, real change points have similar change representations across view directions, but fake change points do not. Finally, we design the change map rendering process based on the built NeRFs and can generate the change map of an arbitrarily specified view direction. To validate the effectiveness, we build a new dataset containing ten scenes covering diverse scenarios with different changing objects. Our approach surpasses state-of-the-art CD methods and NeRF-based methods by a significant margin.