STORM: Segment, Track, and Object Re-Localization from a Single 3D Model

• URL: http://arxiv.org/abs/2511.09771v1
• Date: Fri, 14 Nov 2025 01:08:49 GMT
• Title: STORM: Segment, Track, and Object Re-Localization from a Single 3D Model
• Authors: Yu Deng, Teng Cao, Hikaru Shindo, Jiahong Xue, Quentin Delfosse, Kristian Kersting,
• Abstract summary: STORM is an open-source robust real-time 6D pose estimation system that requires no manual annotation.<n>STORM employs a novel three-stage pipeline combining vision-supervised understanding with self-language feature matching.
• Score: 35.39496117133769
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate 6D pose estimation and tracking are fundamental capabilities for physical AI systems such as robots. However, existing approaches typically rely on a manually annotated segmentation mask of the target in the first frame, which is labor-intensive and leads to reduced performance when faced with occlusions or rapid movement. To address these limi- tations, we propose STORM (Segment, Track, and Object Re-localization from a single 3D Model), an open-source robust real-time 6D pose estimation system that requires no manual annotation. STORM employs a novel three-stage pipeline combining vision-language understanding with self-supervised feature matching: contextual object descriptions guide localization, self-cross-attention mechanisms identify candidate regions, and a segmentation model produces precise masks for accurate pose estimation. Another key innovation is our automatic re-registration mechanism that detects tracking failures through feature similarity monitoring and recovers from severe occlusions or rapid motion. STORM achieves state-of-the-art accuracy on challenging industrial datasets featuring multi-object occlusions, high-speed motion, and varying illumination, while operating at real-time speeds without additional training. This annotation-free approach significantly reduces deployment overhead, providing a practical solution for modern applications, such as flexible manufacturing and intelligent quality control.

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