PruneGround prunes 3D scenes via language-guided VLM, reformulates descriptions with multi-view reasoning, and adapts a spatial LLM to achieve SOTA 3D visual grounding on ScanRefer and most Nr3D/Sr3D settings.
PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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 a generalizable 3DVG framework, PanoGrounder, 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 strong generalization to unseen 3D datasets and text rephrasings.
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
OneCanvas aggregates multi-view 3D patches onto one panoramic canvas with continuous angular placement and 3D embeddings, enabling pretrained VLMs to achieve SOTA on SQA3D and VSI-Bench with an order of magnitude less compute via a new spatial pretraining curriculum.
Survey organizing panoramic scene analysis literature by architectural design and training paradigm, identifying the absence of methods achieving both strict spherical equivariance and full reuse of perspective-pretrained weights, plus five evaluation protocol gaps and a six-point roadmap.
citing papers explorer
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PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding
PruneGround prunes 3D scenes via language-guided VLM, reformulates descriptions with multi-view reasoning, and adapts a spatial LLM to achieve SOTA 3D visual grounding on ScanRefer and most Nr3D/Sr3D settings.
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OneCanvas: 3D Scene Understanding via Panoramic Reprojection
OneCanvas aggregates multi-view 3D patches onto one panoramic canvas with continuous angular placement and 3D embeddings, enabling pretrained VLMs to achieve SOTA on SQA3D and VSI-Bench with an order of magnitude less compute via a new spatial pretraining curriculum.
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Panoramic Scene Analysis: A Survey from Distortion-Aware Engineering to Sphere-Native Foundation Modeling
Survey organizing panoramic scene analysis literature by architectural design and training paradigm, identifying the absence of methods achieving both strict spherical equivariance and full reuse of perspective-pretrained weights, plus five evaluation protocol gaps and a six-point roadmap.