PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding

3D Visual Grounding (3DVG) is a critical bridge from vision-language perception to robotics, requiring both language understandingand 3D scene reasoning. Traditional supervised models leverage explicit3D geometry but exhibit limited generalization, owing to the scarcity of3D vision-language datasets and the limited reasoning capabilities com-pared to modern vision-language models (VLMs). We propose a gener-alizable 3DVG framework, PanoGrounder, that couples multi-modalpanoramic representation with pretrained 2D VLMs for strong vision-language reasoning. Panoramic renderings, augmented with 3D semanticand geometric features, serve as an intermediate representation between2D and 3D, and offer two major benefits: (i) they can be directly fed toVLMs with minimal adaptation and (ii) they retain long-range object-to-object relations thanks to their 360-degree field of view. We devise athree-stage pipeline that places a compact set of panoramic viewpointsconsidering the scene layout and geometry, grounds a text query on eachpanoramic rendering with a VLM, and fuses per-view predictions into asingle 3D bounding box via lifting. Our approach achieves state-of-the-artresults on ScanRefer and Nr3D, and demonstrates strong generalizationto unseen 3D datasets and text rephrasings.