3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object Rearrangement

• URL: http://arxiv.org/abs/2411.03706v1
• Date: Wed, 06 Nov 2024 07:08:41 GMT
• Title: 3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object Rearrangement
• Authors: Ziqi Lu, Jianbo Ye, John Leonard,
• Abstract summary: We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Our method can detect changes in cluttered environments using sparse post-change images within as little as 18s, using as few as a single new image.
• Score: 2.2122801766964795
• License:
• Abstract: We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Leveraging 3DGS's novel view rendering and EfficientSAM's zero-shot segmentation capabilities, we detect 2D object-level changes, which are then associated and fused across views to estimate 3D changes. Our method can detect changes in cluttered environments using sparse post-change images within as little as 18s, using as few as a single new image. It does not rely on depth input, user instructions, object classes, or object models -- An object is recognized simply if it has been re-arranged. Our approach is evaluated on both public and self-collected real-world datasets, achieving up to 14% higher accuracy and three orders of magnitude faster performance compared to the state-of-the-art radiance-field-based change detection method. This significant performance boost enables a broad range of downstream applications, where we highlight three key use cases: object reconstruction, robot workspace reset, and 3DGS model update. Our code and data will be made available at https://github.com/520xyxyzq/3DGS-CD.

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