NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation

• URL: http://arxiv.org/abs/2507.01463v2
• Date: Tue, 02 Sep 2025 11:45:02 GMT
• Title: NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
• Authors: Max Gandyra, Alessandro Santonicola, Michael Beetz,
• Abstract summary: We present a new training-free framework, called: Novel Object Cyclic Threshold based Instance (NOCTIS)<n> NOCTIS integrates two pre-trained models: Grounded-SAM 2 for object proposals with precise bounding boxes and corresponding segmentation masks; and DINOv2 for robust class and patch embeddings.<n>We empirically show that NOCTIS, without further training/fine tuning, attains state-of-the-art results regarding the mean AP score.
• Score: 42.84923737757637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instance segmentation of novel objects instances in RGB images, given some example images for each object, is a well known problem in computer vision. Designing a model general enough to be employed for all kinds of novel objects without (re-) training has proven to be a difficult task. To handle this, we present a new training-free framework, called: Novel Object Cyclic Threshold based Instance Segmentation (NOCTIS). NOCTIS integrates two pre-trained models: Grounded-SAM 2 for object proposals with precise bounding boxes and corresponding segmentation masks; and DINOv2 for robust class and patch embeddings, due to its zero-shot capabilities. Internally, the proposal-object matching is realized by determining an object matching score based on the similarity of the class embeddings and the average maximum similarity of the patch embeddings with a new cyclic thresholding (CT) mechanism that mitigates unstable matches caused by repetitive textures or visually similar patterns. Beyond CT, NOCTIS introduces: (i) an appearance score that is unaffected by object selection bias; (ii) the usage of the average confidence of the proposals bounding box and mask as a scoring component; and (iii) an RGB-only pipeline that performs even better than RGB-D ones. We empirically show that NOCTIS, without further training/fine tuning, attains state-of-the-art results regarding the mean AP score, w.r.t. the best RGB and RGB-D methods on the seven core datasets of the BOP 2023 challenge for the "Model-based 2D segmentation of unseen objects" task.

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