Gaga: Group Any Gaussians via 3D-aware Memory Bank
Keywords: 3D Open-world Segmentation; Gaussian Splatting; Scene Understanding
Abstract: We introduce *Gaga*, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot class-agnostic segmentation models. Contrasted to prior 3D scene segmentation approaches that heavily rely on video object tracking, *Gaga* utilizes spatial information provided by 3D Gaussians and effectively associates object masks across diverse camera poses through a novel 3D-aware memory bank. By eliminating the assumption of continuous view changes in training images, *Gaga* demonstrates robustness to variations in camera poses, particularly beneficial for sparsely sampled images, ensuring precise mask label consistency. Furthermore, *Gaga* accommodates 2D segmentation masks from diverse sources and demonstrates robust performance with different open-world zero-shot class-agnostic segmentation models, significantly enhancing its versatility. Extensive qualitative and quantitative evaluations demonstrate that *Gaga* performs favorably against state-of-the-art methods, emphasizing its potential for real-world applications such as scene understanding and manipulation. The source codes will be made available to the public.
Supplementary Material: zip
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 3192
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