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

TL;DR

PanoGrounder introduces a panoramic scene representation method that leverages pretrained 2D vision-language models for improved 3D visual grounding, enhancing generalization and reasoning in robotics applications.

Contribution

The paper presents a novel panoramic representation framework that integrates 2D VLMs with 3D scene understanding, enabling better generalization and reasoning in 3D visual grounding tasks.

Findings

Achieves state-of-the-art results on ScanRefer and Nr3D datasets.

Demonstrates superior generalization to unseen 3D datasets.

Effectively handles text rephrasings in 3D grounding.

Abstract

3D Visual Grounding (3DVG) is a critical bridge from vision-language perception to robotics, requiring both language understanding and 3D scene reasoning. Traditional supervised models leverage explicit 3D geometry but exhibit limited generalization, owing to the scarcity of 3D vision-language datasets and the limited reasoning capabilities compared to modern vision-language models (VLMs). We propose PanoGrounder, a generalizable 3DVG framework that couples multi-modal panoramic representation with pretrained 2D VLMs for strong vision-language reasoning. Panoramic renderings, augmented with 3D semantic and geometric features, serve as an intermediate representation between 2D and 3D, and offer two major benefits: (i) they can be directly fed to VLMs with minimal adaptation and (ii) they retain long-range object-to-object relations thanks to their 360-degree field of view. We devise a three-stage pipeline that places a compact set of panoramic viewpoints considering the scene layout and geometry, grounds a text query on each panoramic rendering with a VLM, and fuses per-view predictions into a single 3D bounding box via lifting. Our approach achieves state-of-the-art results on ScanRefer and Nr3D, and demonstrates superior generalization to unseen 3D datasets and text rephrasings.