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

• URL: http://arxiv.org/abs/2603.06168v2
• Date: Thu, 12 Mar 2026 14:27:22 GMT
• Title: JOPP-3D: Joint Open Vocabulary Semantic Segmentation on Point Clouds and Panoramas
• Abstract summary: We present JOPP-3D, an open-vocabulary semantic segmentation framework that jointly leverages panoramic and point cloud data.<n>We convert RGB-D panoramic images into their corresponding tangential perspective images and 3D point clouds, then use these modalities to extract and align foundational vision-language features.
• Score: 13.451546315367613
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 semantic 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 tangential perspective images 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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