GOPS: Learning Generative Object Priors for Unsupervised 3D Instance Segmentation

ICLR 2025 Conference Submission883 Authors

Keywords: 3D scene object segmentation, unsupervised learning

Abstract: We study the hard problem of 3D object segmentation in complex point clouds without requiring human labels of 3D scenes for supervision. By relying on the similarity of pretrained 2D features or external signals such as motion to group 3D points as objects, existing unsupervised methods are usually limited to identifying simple objects like cars or their segmented objects are often inferior due to the lack of objectness in pretrained features. In this paper, we propose a new two-stage pipeline called GOPS. The core concept of our method is to learn generative and discriminative object-centric priors as a foundation from object datasets in the first stage, and then to learn multiple objects by querying against the pretrained priors in the second stage. We extensively evaluate our method on two real-world datasets and a newly created synthetic dataset, demonstrating remarkable segmentation performance, clearly surpassing all existing unsupervised methods.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 883