SSR3D-LLM improves fine-grained 3D grounding in unified 3D-LLMs by generating and scoring sequences of latent spatial reasoning steps from the query using fixed Mask3D proposals.
Llm-grounder: Open-vocabulary 3d visual grounding with large language model as an agent
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
TrianguLang achieves state-of-the-art feed-forward text-guided 3D localization and segmentation by using predicted geometry to gate cross-view semantic correspondences without ground-truth poses.
SceneGraphGrounder builds a persistent 3D scene graph from VLM-inferred relations in 2D views and solves grounding via constrained graph alignment, achieving competitive zero-shot results on ScanRefer with only RGB-D input.
citing papers explorer
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SSR3D-LLM: Structured Spatial Reasoning via Latent Steps for Fine-Grained Grounding in Unified 3D-LLMs
SSR3D-LLM improves fine-grained 3D grounding in unified 3D-LLMs by generating and scoring sequences of latent spatial reasoning steps from the query using fixed Mask3D proposals.
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TrianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization
TrianguLang achieves state-of-the-art feed-forward text-guided 3D localization and segmentation by using predicted geometry to gate cross-view semantic correspondences without ground-truth poses.
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SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching
SceneGraphGrounder builds a persistent 3D scene graph from VLM-inferred relations in 2D views and solves grounding via constrained graph alignment, achieving competitive zero-shot results on ScanRefer with only RGB-D input.