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arXiv · 2603.06168

JOPP-3D: Joint Open Vocabulary Semantic Segmentation on Point Clouds and Panoramas

Sandeep Inuganti·Hideaki Kanayama·Kanta Shimizu·Mahdi Chamseddine·Soichiro Yokota·Didier Stricker·Jason Rambach
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Abstract

Semantic segmentation across visual modalities such as 3D point clouds and panoramic images remains a challenging task, primarily due to the scarcity of annotated data and the limited adaptability of fixed-label models. In this paper, we present JOPP-3D, an open-vocabulary segmentation framework that jointly leverages panoramic and point cloud data to enable language-driven scene understanding. We convert RGB-D panoramic images into their corresponding wide field-of-view tangential perspectives and 3D point clouds, then use these modalities to extract and align foundational vision-language features. This allows natural language querying to generate semantic masks on both input modalities. Experimental evaluation on the Stanford-2D-3D-s and ToF-360 datasets demonstrates the capability of JOPP-3D to produce coherent and semantically meaningful segmentations across panoramic and 3D domains. Our proposed method achieves a significant improvement compared to the SOTA in open and closed vocabulary 2D and 3D semantic segmentation.

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Sandeep Inuganti, Hideaki Kanayama, Kanta Shimizu, Mahdi Chamseddine, Soichiro Yokota, Didier Stricker, Jason Rambach. 2026-06-27. JOPP-3D: Joint Open Vocabulary Semantic Segmentation on Point Clouds and Panoramas. https://arxiv.org/abs/2603.06168

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